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Data Annotation Tools for Machine Learning (Evolving Guide)

Choosing the Best Data Annotation Tool for Your Project

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The data annotation tools you use to enrich your data for training and deploying machine learning models can determine success or failure for your AI project. Your tools play an important role in whether you can create a high-performing model that powers a disruptive solution or solves a painful, expensive problem - or end up investing time and resources on a failed experiment.

Choosing your tool may not be a fast or easy decision. The data annotation tool ecosystem is changing quickly as more providers offer options for an increasingly diverse array of use cases. Tooling advancements happen by the month, sometimes by the week. These changes bring improvements to existing tools and new tools for emerging use cases.

The challenge is thinking strategically about your tooling needs now and into the future. New tools, more advanced features, and changes in options, such as storage and security, make your tooling choices more complex. And, an increasingly competitive marketplace makes it challenging to discern hype from real value.

We’ve called this an evolving guide because we will update it regularly to reflect changes in the data annotation tool ecosystem. So be sure to check back regularly for new information, and you can bookmark this page.

The Outsourcers' Guide to Quality

Read the full guide below, or download a PDF version of the guide you can reference later.

In this guide, we’ll cover data annotation tools for computer vision and NLP (natural language processing) for supervised learning .

First, we’ll explain the idea of data annotation tools in more detail, introducing you to key terms and concepts. Next, we will explore the pros and cons of building your own tool versus purchasing a commercially available tool or leveraging open source options.

We’ll give you considerations for choosing your tool and share our short list of the best data annotation tools available. You’ll also get a short list of critical questions to ask your tool provider.

Table of Contents

Introduction: will this guide be helpful to me, the basics: data annotation tools and machine learning, a critical choice: build vs. buy, how to choose a data annotation tool, the best data annotation tools: commercial and open source, iteration & evolution: changing data annotation needs, new tools, questions to ask your data annotation tool provider, tool agnostic: the cloudfactory advantage.

  • Introduction
  • Build vs. Buy
  • How to Choose
  • Best Data Annotation Tools
  • Iteration & Evolution
  • Questions to Ask
  • CloudFactory Advantage

This guide will be helpful if :

  • You are beginning a machine learning project and have data you want to clean and annotate to train, test, and validate your model.
  • You are working with a new data type and need to understand the best tools available for annotating that data.
  • Your data annotation needs have evolved (e.g., you need to add features to your annotation) and want to learn about tools that can handle what you’re doing today and what you’re adding to your process.
  • You are in the production stage and must verify models using human-in-the-loop .

What’s data annotation?

In machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are marking - labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Once your model is deployed, you want it to recognize those features on its own and make a decision or take some action as a result.

Annotated data reveals features that will train your algorithms to identify the same features in data that has not been annotated. Data annotation is used in supervised learning and hybrid, or semi-supervised, machine learning models that involve supervised learning.

What’s a data annotation tool?

A data annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to annotate production-grade training data for machine learning. While some organizations take a do-it-yourself approach and build their own tools, there are many data annotation tools available via open source or freeware.

They are also offered commercially, for lease and purchase. Data annotation tools are generally designed to be used with specific types of data, such as image, video, text, audio, spreadsheet, or sensor data. They also offer different deployment models, including on-premise, container, SaaS (cloud), and Kubernetes.

Data annotation tools have these key elements: They can be used to annotate many data types, including text, image, video, audio, time-series, and sensor data. They support annotation for 2-D, 3-D, video, audio, transcription, and text. You can buy a commercially-available data annotation tool, you can take a do-it-yourself approach and build your own, or you can use open source or freeware to create and tailor a data annotation tool for your use case.

6 Important Data Annotation Tool Features

1) dataset management.

Annotation begins and ends with a comprehensive way of managing the dataset you plan to annotate. As a critical part of your workflow, you need to ensure that the tool you are considering will actually import and support the high volume of data and file types you need to label. This includes searching, filtering, sorting, cloning, and merging of datasets. 

Different tools can save the output of annotations in different ways, so you’ll need to make sure the tool will meet your team’s output requirements. Finally, your annotated data must be stored somewhere. Most tools will support local and network storage, but cloud storage - especially your preferred cloud vendor - can be hit or miss, so confirm support-file storage targets.

2) Annotation methods

This is obviously the core feature of data annotation tools - the methods and capabilities to apply labels to your data. But not all tools are created equal in this regard. Many tools are narrowly optimized to focus on specific types of labeling, while others offer a broad mix of tools to enable various types of use cases.

Nearly all offer some type of data or document classification to guide how you identify and sort your data. Depending on your current and anticipated future needs, you may wish to focus on specialists or go with a more general platform. The common types of annotation capabilities provided by data annotation tools include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific annotation types.

Here are just a few examples:

  • Image or video: Bounding boxes, polygons, polylines, classification, 2-D and 3-D points, or segmentation (semantic or instance), tracking, transcription, interpolation, or transcription.
  • Text: Transcription, sentiment analysis, net entity relationships (NER), parts of speech (POS), dependency resolution, or coreference resolution.
  • Audio: Audio labeling, audio to text, tagging, time labeling

An emerging feature in many data annotation tools is automation, or auto-labeling . Using AI, many tools will assist your human labelers to improve their annotations (e.g. automatically convert a four-point bounding box to a polygon), or even automatically annotate your data without a human touch. Additionally, some tools can learn from the actions taken by your human annotators, to improve auto-labeling accuracy.

Some annotation tasks are ripe for automation. For example, if you use pre-annotation to tag images, a team of data labelers can determine whether to resize or delete a bounding box. This can shave time off the process for a team that needs images annotated at pixel-level segmentation. Still, there will always be exceptions, edge cases, and errors with automated annotations, so it is critical to include a human-in-the-loop approach for both quality control and exception handling.

Automation also can refer to the availability of developer interfaces to run the automations. That is, an application programming interface (API) and software development kit (SDK) that allow access to and interaction with the data.

3) Data quality control 

The performance of your machine learning and AI models will only be as good as your data. Data annotation tools can help manage the quality control (QC) and verification process. Ideally, the tool will have embedded QC within the annotation process itself.

For example, real-time feedback and initiating issue tracking during annotation is important. Additionally, workflow processes such as labeling consensus, may be supported. Many tools will provide a quality dashboard to help managers view and track quality issues, and assign QC tasks back out to the core annotation team or to a specialized QC team.

4) Workforce management

Every data annotation tool is meant to be used by a human workforce - even those tools that may lead with an AI-based automation feature. You still need humans to handle exceptions and quality assurance as noted before. As such, leading tools will offer workforce management capabilities, such as task assignment and productivity analytics measuring time spent on each task or sub-task.

Your data labeling workforce provider may bring their own technology to analyze data that is associated with quality work. They may use technology, such as webcams, screenshots, inactivity timers, and clickstream data to identify how they can support workers in delivering quality data annotation.

Most importantly, your workforce must be able to work with and learn the tool you plan to use. Further, your workforce provider should be able to monitor worker performance and work quality and accuracy . It’s even better when they offer you direct visibility, such as a dashboard view, into the productivity of your outsourced workforce and the quality of the work performed.

5) Security

Whether annotating sensitive protected personal information (PPI) or your own valuable intellectual property (IP), you want to make sure that your data remains secure.  Tools should limit an annotator’s viewing rights to data not assigned to her, and prevent data downloads. Depending on how the tool is deployed, via cloud or on-premise, a data annotation tool may offer secure file access (e.g., VPN).

For use cases that fall under regulatory compliance requirements, many tools will also log a record of annotation details, such as date, time, and the annotation author. However, if you are subject to HIPAA, SOC 1, SOC 2, PCI DSS, or SSAE 16 regulations, it is important to carefully evaluate whether your data annotation tool partner can help you maintain compliance.

6) Integrated labeling services

As mentioned earlier, every tool requires a human workforce to annotate data, and the people and technology elements of data annotation are equally important. As such, many data annotation tool providers offer a workforce network to provide annotation as a service. The tool provider either recruits the workers or provides access to them via partnerships with workforce providers.

While this feature makes for convenience, any workforce skill and capability should be evaluated separately from the tool capability itself. The key here is that any data annotation tool should offer the flexibility to use the tool vendor’s workforce or the workforce of your choice, such as a group of employees or a skilled, professionally managed data annotation team.

6 key features for data annotation tools

Download the PDF version here

Just a few years ago, there weren’t many data annotation tools available to buy. Most early movers had to use what was available via open source or build their own tools if they wanted to apply AI to solve a painful business problem or create a disruptive product.

Starting in about 2018, a wave of commercial data annotation tools became available, offering full-featured, complete-workflow commercial tools for data labeling. The emergence of these third-party, professionally developed tools began to force a discussion within data science and AI project teams around whether to continue to take a DIY approach and build their own tools or purchase one. And if the answer was to purchase a data annotation tool, they still needed to decide how to select the right tool for their project.

When to build your own data annotation tool

Even though there are third-party tools available to purchase, it may still make business sense to build a data annotation tool. Building your own tool provides you with the ultimate level of control - from the end-to-end workflow of the annotation process, to the type of data you can label and the resulting outputs.

And, as you continue to iterate your business processes and your machine learning models, you can make changes quickly, using your own developers and setting your own priorities. You also can apply technical controls to meet your company’s unique security requirements. And finally, an organization may want to include all of their AI tooling in their intellectual property, and building a data annotation tool internally allows them to do that.

However, when you’re building a tool, you often face many unknowns at the beginning, and the scope of tool requirements can quickly shift and evolve, causing teams to lose time. There is also the additional overhead of standing up the infrastructure needed to develop and run the tooling, as well as development resources required to maintain the data annotation tool.

When to buy a data annotation tool

Generally, buying a tool that is commercially available can be less expensive because you avoid the upfront development and ongoing direct support expenses. This allows you to focus your time and resources on your core project:

  • Without the distraction of supporting and expanding features and capabilities for an in-house tool that is custom-built; and
  • Without bearing the ongoing burden of funding the tool to ensure its continued success.

Buying an existing data annotation tool can accelerate your project timeline, enabling you to get started more quickly with an enterprise-ready, tested data labeling tool. Additionally, tooling vendors work with many different customers and can incorporate industry best practices into their data annotation tools. Finally, when it comes to features, you can usually configure a commercial tool to meet your needs, and there are more than one of these kinds of tools available for any data annotation workload.

Of course, a third-party data annotation tool is not typically built with your specific use case or workflow in mind, so you may sacrifice some level of control and customization.  And as your project or product evolves, you may find that your data annotation tool requirements change over time. If the tool you originally bought doesn’t support your new requirements, you will need to build or buy integrations or separate tools to meet your new needs.

The open source option for data annotation tools

There are open source data annotation tools available. You can use an open source tool and support it yourself, or use it to jump-start your own build effort. There are many open source projects for tooling related to image, video, natural language processing, and transcription, and such a tool can be a great option for a one-time project.

But often an open source tool will present challenges when you try to scale your project into production, as these tools are typically designed around a single user and offer poor or insufficient workflow options for a team of data labelers. Additionally, you need to have the technical expertise on hand to deploy and maintain the tool. Many people are lured by open source being “free” and forget to factor in the total cost of ownership - the time and expense required to develop the workflows, workforce management, and quality assurance management that are necessary and inherently present in commercial data annotation tools.

Growth stage as an indicator for buy vs. build

Another helpful way to look at the build versus buy question is to consider your stage of organizational growth.

  • Start: In the early stages of growth, freeware or open source data annotation tools can make sense if you have development resources and you want to build your own tool. You also could choose a workforce that provides a data annotation tool. But be careful not to unnecessarily tie your data annotation tool to your workforce; you’ll want the flexibility to make changes later.
  • Scale: If you’re at the growth stage, you might want the ability to customize commercial data annotation tools, and you can do that with little to no development resources. If you build, you’re going to need to allocate resources to maintain and improve your tool. Keep in mind to consider existing storage and, if you use a cloud vendor, make sure they can work with your requirements.
  • Sustain: When you’re operating at scale, it’s likely to be important for you to have control, enhanced data security, or the agility to make changes, such as feature enhancements. In that case, open source tools that are self-built and managed might be your best bet.

When you are looking for a data annotation tool, an important consideration is the growth stage of your organization. In the early stages of growth, open source or crowdsourcing make sense. At the growth stage, consider commercial data annotation tools or building your own. At scale, you might want the control, enhanced data security, or agility you get from building your own data annotation tool.

There is a lot to consider in the build vs buy equation. If, after considering all of the factors, you conclude that the time and expense is not worth a DIY approach and the potential gain of customization and retaining IP, then the next decision you will need to make is about which commercial tool you choose to purchase. In this section we will explore some of those considerations.

1) What is your use case?

First and foremost, the type of data you want to annotate and your business processes for doing the work will influence your tool choice. There are tools for labeling text, image, and video. Some image labeling tools also have video labeling capabilities.

Of note, more and more data annotation tool providers are realizing they want to do more than provide a singular tool - they want to provide a holistic technology platform for data annotation for machine learning. A simple data annotation tool provides features that make it easy to enrich the data. A platform provides an environment that supports the data annotation and AI development process.

A platform may include features such as multiple annotation options (e.g., 2-D, 3-D, audio, text), more than one storage option (e.g., local, network, cloud), or quality control workflow. It also may be able to accept pre-annotated data or may include embedded neural networks that learn from manual annotations made using the platform. Considering a platform may be helpful if you anticipate your project or product needs evolving significantly over time, as a platform may provide greater flexibility in the future.

2) How will you manage quality control requirements?

How you want to measure and control quality is also an important consideration for your data annotation tool. Many commercially-available tools have quality control (QC) features built-in that can review, provide feedback, and correct tasks. For example, QC options might include:

  • Consensus - Annotator agreement determines quality. For example, when annotators disagree on an edge case, the task is passed to a third annotator or more until a percentage of certainty is reached. Feedback can be provided to the workforce to learn how to correctly annotate those edge cases.
  • Gold standard - The correct answer is known. The tool measures quality based on correct and incorrect tasks.
  • Sample review - The tools reviews a random sample of completed tasks for accuracy.
  • Intersection over union (IoU) - This is a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

Some tools can even automate a portion of your QC. However, whenever you are using automation for a portion of your data labeling process, you will need people to perform QC on that work. For example, optical character recognition (OCR) software has an error rate of 1% to 3% per character. On a page with 1,800 characters, that’s 18-54 errors. For a 300-page book, that’s 5,400-16,200 errors. You will want a process that includes a QC layer performed by skilled labelers with context and domain expertise.

3) Who will be using the tool?

An often overlooked aspect of tool selection is workforce. Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourcing provider, your workforce will need access to and training to use your data annotation tool, with specific task instructions unique to your use case. Make sure you take into account the answers to these questions:

  • Do you have access to a workforce that has pre-existing knowledge of viable commercial tools for your project?
  • Does that team have prior experience using the tool(s) you are considering?
  • If not, do you have detailed documentation and a proven training approach to bring the workforce up to speed?
  • Do you have a process by which you can ensure the required level of quality for your project?

4) Do you need a vendor or a partner?

The company you buy a data annotation tool from can be just as important as the tool itself. Here, you’ll want to consider how easy it is to do business with the company that’s providing the tool and their openness for collaboration. AI development is an iterative process, and you will need to make changes along the way. Are they willing to consider feedback or ideas for new features for their tool that would make your tasks easier or make your AI models run cleaner and with better results? Aim to find a partner who is willing to work with you on such things, not simply a vendor to provide a tool.

As you research your workforce options, you may discover some data labeling services that provide their own tool. However, be careful not to tie your tool to your workforce unnecessarily. You’ll want the flexibility to change either your workforce or your tool, based on your business needs and the solutions available to you, especially as new tools and workforce options emerge. A data labeling service should be able to provide best practices and share recommendations for choosing your tool based on their workforce strategy.

Also, keep in mind that your annotation tasks are likely to change over time. Every machine learning modeling task is different. The set of instructions you are using to collect, clean, and annotate your data today may change in the coming weeks - even days. Anticipating those changes is helpful, and you’ll want to consider that when you’re making the decision about the data annotation tool you select and the workforce that will use it to label your data.

Here’s a closer look at some of the data annotation tools we consider to be among the best available on the market today.

Commercial Data Annotation Tools

Commercially-viable data annotation tools are likely your best choice, particularly if your company is at the growth or enterprise stage. If you are operating at scale and want to sustain that growth over time, you can get commercially-available tools and customize them with few development resources of your own.

Open Source Data Annotation Tools

Open source data annotation tools allow you to use or modify the source code. You can change or customize features to fit your needs. Developers who use open source tools are part of a collaborative community of users who can share use cases, best practices, and feature improvements made by altering the original source code.

Open source tools can give you more control over features and can provide great flexibility as your tasks and data operations evolve. However, using open source tools comes with the same commitment as building your own tool. You will have to make investments to maintain the platform over time, which can be costly.

While open source tools can be good for learning or testing early versions of a commercial application, they often present barriers to scale. This is because most open source tools are not comprehensive labeling solutions and lack robust dataset management, label automation, or other features that drive efficiency (like data clustering). In addition, few open source tools provide quality assurance workflows or accuracy analytics which can hinder data quality.

It’s important to know that open source communities provide support mostly via on-line documentation, FAQs, and tutorials. There are no support numbers to call and some open source tools don’t provide data privacy and security measures needed to comply with GDPR and HIPAA.

There are several open source data annotation tools available, many of which have been available for years and have improved over time.

You will uncover buy vs. build implications throughout your product development lifecycle. From sourcing the data to labeling, modeling, deployment, and improvements - your data annotation tool plays a key role in your project’s success. That’s why your tool choice is so important - because it affects your workflow from the beginning stages of model development through model testing and into production.

With a market size of USD $805.6 million in 2022 , data annotation tools will expand as adoption of data annotation tools increases in the automotive, retail, and healthcare industries. As new options emerge, you may want to consider what is available to you.

Why change data annotation tools?

As you train, test, and validate your model - and even as you tune it in production, your data annotation needs may change. A tool that was built for your first purpose might not serve you as well in the future as your use case, tasks, and business rules evolve. That’s why it’s important to avoid getting into a long-term contract with a single tool or workforce provider - or tying your tool to your workforce.

Here are a few examples of reasons you might want to change your tool during a project:

  • You began building a tool but are now considering buying because commercial tools have added new features that meet your needs.
  • The tool doesn’t have the automation or the automation features you want.
  • Your cost increases for access to the commercial tool.

How do I change data annotation tools?

When you change your data annotation tool in the middle of training or production, you’ll likely ask the same questions you’d ask if you were buying the tool for a new project. However, there will be considerations regarding the ease of transferring your data into a new tool and resuming data annotation in the new tool.

For example, you will have to anticipate and manage details related to:

  • Introducing a different data ingestion pipeline
  • How data is stored
  • Output format
  • Use of a new tool - and training your data workers to use it
  • Your workforce provider’s technology to track the quality and productivity of its workers, and how they capture the data required to do it.

While we know it’s important to be flexible when it comes to your data annotation tool, we have yet to learn how long one tool can meet your needs and how long you should wait before evaluating your options again. The data annotation tool ecosystem is just gathering steam, and those who were among the first teams to monetize their data annotation tools are just starting to renew contracts with their earliest adopters.

This is one aspect of the market we’re watching so we can provide exceptional consultative service to our clients and ensure they are using the best-fit tool for their needs.

Here are questions to keep in mind when you’re speaking with a data annotation tool provider:

Strategic Approach

  • Of all of the features available with your tool, what does your team consider to be your tool’s specialty - and why?
  • How long have you been building, maintaining, and supporting this data annotation tool?
  • How is your tool different from other commercially-available tools?
  • Do you consider your product to be a tool or a platform? What other aspects of the machine learning data labeling process does your tool support?
  • Is your team open to receiving feedback about your data annotation tool, its features, and ways it could be improved to better serve the needs of our use case?
  • What are your pricing methods? (e.g., monthly, annual, by annotation, by worker)

Key Features

  • Do you offer dataset management?
  • Where can files be stored? What capacity does the tool support, in terms of how much data can be moved into the tool? Can I upload pre-annotated images into the tool?
  • Do you offer an API and/or SDK? If so, how robust are they?
  • Do you offer data management?
  • Can I bulk upload classes and attributes into the tool?
  • Does your tool allow us to deploy a large and growing workforce to use it?
  • What security compliance or certifications does your tool have?
  • Is quality control (QC) built into your tooling platform? What does that workflow look like?
  • What kind of quality assurance (QA) do you provide?

Machine Learning

  • Have you built any AI into your tool?
  • Can I bring my own algorithm and plug it into your tool?

Though the specific tools suggested above are a great place to start, it’s best to avoid dependence on any single platform for your data annotation needs. After all, no two datasets present exactly the same challenges, and no particular tool will be the best option in all circumstances. Because training data challenges are unique and dynamic in nature, tying your workforce to one tool can be a strategic liability.

For a more flexible approach to labeling text, images, and video, you’ll need to develop a versatile team that can adapt to new tools. At CloudFactory, this emphasis on versatility guides how we select and train our cloud workers. We hire team members with the skills to work on any platform our clients prefer. No matter the tool you use or the type of training data you need, we have workers ready and able to get started.

The People + Process Component

The maturity of your data annotation tool and its features impact how you and your data workforce will design workflow, quality control, and many other aspects of your data work. A tool that doesn’t take your workforce and your processes into consideration will cost you time and efficiency in building workarounds for things that you’ll wish were native within the tool.

CloudFactory delivers the people and the process, and we know data annotation because we’ve been doing it for the better part of a decade, working remotely for our clients. Our data annotation teams are vetted, trained, and actively managed to deliver higher engagement, accountability, and quality.

  • Work from anywhere - We work how you work, as an extension of your team. We can use any tool and follow the rules you set. Using our proprietary platform, you have direct communication with a team leader to provide feedback. Workers can share their observations to drive improved processes, higher productivity, and better quality.
  • Scale the work - We can flex up or down, based on your business requirements.
  • Select and train top-notch workers - Our workforce strategy values people, and we make sure workers understand the importance of the tasks they are doing for your business. We monitor worker performance for productivity and quality, and our team leaders come alongside workers to train and encourage them.
  • Flexible pricing model - You can scale work up or down without renegotiating your contract. We do not lock you into a long-term contract or tie our workforce to your tool.

Are you ready to select the right data annotation tool? Find out how we can help you save time and money.

Reviewers Anthony Scalabrino , sales engineer at CloudFactory , a provider of professionally managed teams for data annotation for machine learning.

Nir Buschi , Co-founder & Chief Business Officer at Dataloop AI , an enterprise-grade data platform for AI systems in development and in production, providing an end-to-end data workflow including data annotation, quality control, data management, automation pipelines and autoML.

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Frequently asked questions, what is annotated data.

In supervised or semi-supervised machine learning, annotated data is labeled, tagged, or processed for the features you want your machine learning system to learn to recognize. An example of annotated data is sensor data from an autonomous vehicle, where the data has been enriched to show exactly where there are pedestrians and other vehicles.

What is a data annotator?

A data annotator is: 1) someone who works with data and enriches it for use with machine learning; or 2) an auto labeling feature, or automation, that is built into a data annotation tool to enrich data. That automation is powered by machine learning that makes predictions about your annotations based on the training data it has consumed and the tuning of the model during testing and validation.

What is data annotation?

In supervised or semi-supervised machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are enriching - also known as labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Ideally, once you deploy your model, the machine will be able to recognize those features on its own and make a decision or take some action as a result.

What are data annotation tools?

Data annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to label or annotate production-grade training data for machine learning. They can be available via open source or freeware, or they may be offered commercially, for lease. Data annotation tools are designed to be used with specific types of data, such as image, text, audio, spreadsheet, sensor, photogrammetry, or point-cloud data.

What is an image annotation tool?

An image annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label, tag, or annotate images or frame-by-frame video for production-grade training data for machine learning. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance), or transcription. Some image annotation tools include quality control features such as intersection over union (IoU), a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

What’s the best image annotation tool?

The best image annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. Dataloop , Encord , Hasty , Labelbox , Pix4D , Pointly , and Segments.ai offer commercial annotation tools to label images that are used to train, test, and validate machine learning algorithms. CVAT and QGIS are open source tools you can use and customize for your own image annotation needs.

What is a video annotation tool?

A video annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label or annotate video or frame-by-frame images from video for production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially, for lease. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance).

What’s an online annotation tool?

An online annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to label or annotate production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially. Online annotation tools are designed to be used with specific types of data, such as image, text, video, audio, spreadsheet, or sensor data.

What are text annotation tools?

Text annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to annotate production-grade training data for machine learning. This process also can be called labeling, tagging, transcribing, or processing. Text annotation tools can be available via open source or freeware, or they may be offered commercially.

Is there a list of video annotation tools?

Dataloop , Encord , Hasty , Labelbox , and Segments.ai offer commercial annotation tools that can be used to label video to train, test, and validate machine learning algorithms. CVAT is an open source video annotation tool you can use or customize for your own video annotation needs. The best video annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements.

What’s the best text annotation tool?

The best text annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. DatasaurAI and Labelbox offer commercial annotation tools that can be used to analyze language and sentiment to train, test, and validate machine learning algorithms.

  • Accelerated Annotation
  • Workforce Plus
  • Data Labeling Managed Workforce
  • Data Labeling Guide
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data annotation tools list

What is Data Annotation?

data annotation tools list

Building an AI or ML model that acts like a human requires large volumes of training data . For a model to make decisions and take action, it must be trained to understand specific information. Data annotation is the categorization and labeling of data for AI applications. Training data must be properly categorized and annotated for a specific use case. With high-quality, human-powered data annotation, companies can build and improve AI implementations. The result is an enhanced customer experience solution such as product recommendations, relevant search engine results, computer vision, speech recognition, chatbots, and more. There are several primary types of data: text, audio, image, and video

Text Annotation

The most commonly used data type is text - according to the 2020 State of AI and Machine Learning report , 70% of companies rely on text. Text annotations include a wide range of annotations like sentiment, intent, and query.

Sentiment Annotation

Sentiment analysis assesses attitudes, emotions, and opinions, making it important to have the right training data. To obtain that data, human annotators are often leveraged as they can evaluate sentiment and moderate content on all web platforms, including social media and eCommerce sites, with the ability to tag and report on keywords that are profane, sensitive, or neologistic, for example.

Intent Annotation

As people converse more with human-machine interfaces, machines must be able to understand both natural language and user intent. Multi-intent data collection and categorization can differentiate intent into key categories including request, command, booking, recommendation, and confirmation.

Semantic Annotation

Semantic annotation both improves product listings and ensures customers can find the products they’re looking for. This helps turn browsers into buyers. By tagging the various components within product titles and search queries, semantic annotation services help train your algorithm to recognize those individual parts and improve overall search relevance.

Named Entity Annotation

Named Entity Recognition (NER) systems require a large amount of manually annotated training data. Organizations like Appen apply named entity annotation capabilities across a wide range of use cases, such as helping eCommerce clients identify and tag a range of key descriptors, or aiding social media companies in tagging entities such as people, places, companies, organizations, and titles to assist with better-targeted advertising content.

Real World Use Case: Improving Search Quality for Microsoft Bing in Multiple Markets

Microsoft's Bing search engine required large-scale datasets to continuously improve the quality of its search results – and the results needed to be culturally relevant for the global markets they served. We delivered results that surpassed expectations. Beyond delivering project and program management, we provided the ability to grow rapidly in new markets with high-quality data sets. (Read the full case study here)

Audio Annotation

Audio annotation is the transcription and time-stamping of speech data, including the transcription of specific pronunciation and intonation, along with the identification of language, dialect, and speaker demographics. Every use case is different, and some require a very specific approach: for example, the tagging of aggressive speech indicators and non-speech sounds like glass breaking for use in security and emergency hotline technology applications.

Real World Use Case: Dialpad’s transcription models leverage our platform for audio transcription and categorization

Dialpad improves conversations with data. They collect telephonic audio, transcribe those dialogs with in-house speech recognition models, and use natural language processing algorithms to comprehend every conversation. They use this universe of one-on-one conversation to identify what each rep–and the company at large–is doing well and what they aren’t, all with the goal of making every call a success. Dialpad had worked with a competitor of Appen for six months but were having trouble reaching an accuracy threshold to make their models a success. It took just a couple weeks for the change to bear fruit for Dialpad and to create the transcription and NLP training data they needed to make their models a success. (Read the full case study here)

Image Annotation

Image annotation is vital for a wide range of applications, including computer vision, robotic vision, facial recognition, and solutions that rely on machine learning to interpret images. To train these solutions, metadata must be assigned to the images in the form of identifiers, captions, or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort produce, to healthcare applications that auto-identify medical conditions, there are many use cases that require high volumes of annotated images. Image annotation increases precision and accuracy by effectively training these systems.

Appen image annotation facial recognition

Real World Use Case: Adobe Stock Leverages Massive Asset Profile to Make Customers Happy

One of Adobe’s flagship offerings is Adobe Stock, a curated collection of high-quality stock imagery. The library itself is staggeringly large: there are over 200 million assets (including more than 15 million videos, 35 million vectors, 12 million editorial assets, and 140 million photos, illustrations, templates, and 3D assets). Every one of those assets needs to be discoverable. Appen provided highly accurate training data to create a model that could surface these subtle attributes in both their library of over a hundred million images, as well as the hundreds of thousands of new images that are uploaded every day. That training data powers models that help Adobe serve their most valuable images to their massive customer base. Instead of scrolling through pages of similar images, users can find the most useful ones quickly, freeing them up to start creating powerful marketing materials. (Read the full case study here)

Video Annotation

Human-annotated data is the key to successful machine learning. Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity. For example, when determining whether a search engine result is relevant, input from many people is needed for consensus. When training a computer vision or pattern recognition solution, humans are needed to identify and annotate specific data, such as outlining all the pixels containing trees or traffic signs in an image. Using this structured data, machines can learn to recognize these relationships in testing and production.

Real World Use Case: HERE Technologies Creates Data to Fine-Tune Maps Faster Than Ever

With a goal of creating three-dimensional maps that are accurate down to a few centimeters, HERE has remained an innovator in the space since the mid-’80s, giving hundreds of businesses and organizations detailed, precise and actionable location data and insights. HERE has an ambitious goal of annotating tens of thousands of kilometers of driven roads for the ground truth data that powers their sign-detection models. Parsing videos into images for that goal, however, is simply untenable. Our Machine Learning assisted Video Object Tracking solution presented a perfect solution to this lofty ambition. That’s because it combines human intelligence with machine learning to drastically increase the speed of video annotation. (Read the full case study here)

What Appen Can Do For You

At Appen, our data annotation experience spans over 20 years. By combining our human-assisted approach with machine-learning assistance, we give you the high-quality training data you need. Our text annotation, image annotation, audio annotation, and video annotation will give you the confidence to deploy your AI and ML models at scale. Whatever your data annotation needs may be, our platform and managed service team are standing by to assist you in both deploying and maintaining your AI and ML projects.

Contact us today

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data annotation tools list

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Data Annotation Tutorial: Definition, Tools, Datasets

Nilesh Barla

Data is an integral part of all machine learning and deep learning algorithms .

It is what drives these complex and sophisticated algorithms to deliver state-of-the-art performances.

If you want to build truly reliable AI models , you must provide the algorithms with data that is properly structured and labeled.

And that's where the process of data annotation comes into play.

You need to annotate data so that the machine learning systems can use it to learn how to perform given tasks.

Data annotation is simple, but it might not be easy 😉 Luckily, we are about to walk you through this process and share our best practices that will save you plenty of time (and trouble!).

Here’s what we’ll cover:

What is data annotation?

Types of data annotations.

  • Automated data annotation vs. human annotation

V7 data annotation tutorial

Solve any video or image labeling task 10x faster and with 10x less manual work.

Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.

Ready to streamline AI product deployment right away? Check out:

  • V7 Model Training
  • V7 Workflows
  • V7 Auto Annotation
  • V7 Dataset Management

Essentially, this comes down to labeling the area or region of interest—this type of annotation is found specifically in images and videos. On the other hand, annotating text data largely encompasses adding relevant information, such as metadata, and assigning them to a certain class.

In machine learning , the task of data annotation usually falls into the category of supervised learning, where the learning algorithm associates input with the corresponding output, and optimizes itself to reduce errors.

Here are various types of data annotation and their characteristics.

Image annotation

Image annotation is the task of annotating an image with labels. It ensures that a machine learning algorithm recognizes an annotated area as a distinct object or class in a given image.

It involves creating bounding boxes (for object detection ) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. In V7, you can also annotate the image using tools such as keypoint, 3D cuboids, polyline, keypoint skeleton, and a brush.

💡 Pro tip: Check out 13 Best Image Annotation Tools to find the annotation tool that suits your needs.

Image annotation is often used to create training datasets for the learning algorithms.

Those datasets are then used to build AI-enabled systems like self-driving cars, skin cancer detection tools, or drones that assess the damage and inspect industrial equipment.

💡 Pro tip: Check out AI in Healthcare and AI in Insurance to learn more about AI applications in those industries.

Now, let’s explore and understand the different types of image annotation methods.

  • Bounding box

The bounding box involves drawing a rectangle around a certain object in a given image. The edges of bounding boxes ought to touch the outermost pixels of the labeled object.

Otherwise, the gaps will create IoU (Intersection over Union) discrepancies and your model might not perform at its optimum level.

💡 Pro tip: Read Annotating With Bounding Boxes: Quality Best Practices to learn more.

The 3D cuboid annotation is similar to bounding box annotation, but in addition to drawing a 2D box around the object, the user has to take into account the depth factor as well.  It can be used to annotate objects such on flat planes that need to be navigated, such as cars or planes, or objects that require robotic grasping.

You can annotate with cuboids to build to train the following model types:

- Object Detection

- 3D Cuboid Estimation

- 6DoF Pose Estimation

Creating a 3D cuboid in V7 is quite easy, as V7's cuboid tool automatically connects the bounding boxes you create by adding a spatial depth.  Here's the image of a plane annotated using cuboids.

Plane annotation using 3D cuboid in V7

While creating a 3D cuboid or a bounding box, you might notice that various objects might get unintentionally included in the annotated region. This situation is far from ideal, as the machine learning model might get confused and, as a result, misclassify those objects.

Luckily, there's a way to avoid this situation—

And that's where polygons come in handy. What makes them so effective is their ability to create a mask around the desired object at a pixel level.

V7 offers two ways in which you can create pixel-perfect polygon masks.

a) Polygon tool

You can pick the tool and simply start drawing a line made of individual points around the object in the image. The line doesn't need not be perfect, as once the starting and ending points are connected around the object, V7 will automatically create anchor points that can be adjusted for the desired accuracy.

Once you've created your polygon masks, you can add a label to the annotated object.

Apples annotated using the polygon tool un V7

b) Auto-annotation tool

V7's auto-annotate tool is an alternative to manual polygon annotation that allows you to create polygon and pixel-wise masks 10x faster.

💡 Pro tip: Ready to train your models? Have a look at Mean Average Precision (mAP) Explained: Everything You Need to Know.

Keypoint tool

Keypoint annotation is another method to annotate an object by a series or collection of points.

This type of method is very useful in hand gesture detection, facial landmark detection, and motion tracking. Keypoints can be used alone, or in combination to form a point map that defines the pose of an object.

Keypoint annotation of joints in V7

Keypoint skeleton tool

V7 also offers keypoint skeleton tool—a network of keypoints connected by vectors, used specifically for pose estimation.

It is used to define the 2D or 3D pose of a multi-limbed object. Keypoints skeletons have a defined set of points that can be moved to adapt to an object’s appearance.

You can use keypoint annotation to train a machine learning model to mimic human pose and then extrapolate their functionality for task-specific applications, for example, AI-enabled robots.

See how you can annotate your image and video data using the keypoint skeleton in V7.

💡 Pro tip: Check out 27+ Most Popular Computer Vision Applications and Use Cases.

Polyline tool

Polyline tool allows the user to create a sequence of joined lines.

You can use this too by clicking around the object of interest to create a point. Each point will create a line by joining the current point with the previous one. It can be used to annotate roads, lane marking, traffic signs, etc.

Bike lane annotation using polyline tool in V7

Semantic segmentation

Semantic segmentation is the task of grouping together similar parts or pixels of the object in a given image. Annotating data using this method allows the machine learning algorithm to learn and understand a specific feature, and it can help it to classify anomalies.

Semantic segmentation is very useful in the medical field, where radiologists use it to annotate X-Ray, MRI, and CT scans to identify the region of interest. Here's an example of a chest X-Ray annotation.

AI chest X-Ray annotation analysis in V7

If you are looking for medical data, check out our list of healthcare datasets and see how you can annotate medical imaging data using V7.

Video annotation

Similar to image annotation,   video annotation is the task of labeling sections or clips in the video to classify, detect or identify desired objects frame by frame.

Video annotation uses the same techniques as image annotation like bounding boxes or semantic segmentation, but on a frame-by-frame basis. It is an essential technique for computer vision tasks such as localization and object tracking.

Here's how V7 handles video annotation .

Tackle any video format frame by frame. Use AI models to label sequences. Interpolate any annotation.

Mockups of videos annotated on V7 platform

Text annotation

Data annotation is also essential in tasks related to Natural Language Processing (NLP).

Text annotation refers to adding relevant information about the language data by adding labels or metadata. To get a more intuitive understanding of text annotation let's consider two examples.

1. Assigning Labels

Adding labels means assigning a sentence with a word that describes its type. It can be described with sentiments, technicality, etc. For example, one can assign a label such as “happy” to this sentence “I am pleased with this product, it is great”.

2. Adding metadata

Similarly, in this sentence “I’d like to order a pizza tonight”, one can add relevant information for the learning algorithm, so that it can prioritize and focus on certain words. For instance, one can add information like “I’d like to order a pizza ( food_item ) tonight ( time )”.

Now, let’s briefly explore various types of text annotations.

Sentiment Annotation

Sentiment annotation is nothing but assigning labels that represent human emotions such as sad, happy, angry, positive, negative, neutral, etc. Sentiment annotation finds application in any task related to sentiment analysis (e.g. in retail to measure customer satisfaction based on facial expressions)

Intent Annotation

The intent annotation also assigns labels to the sentences, but it focuses on the intent or desire behind the sentence. For instance, in a customer service scenario, a message like “I need to talk to Sam ”, can route the call to Sam alone, or a message like “I have a concern about the credit card ” can route the call to the team dealing credit card issues.

Named Entity Annotation (NER)

Named entity recognition (NER) aims to detect and classify predefined named entities or special expressions in a sentence.

It is used to search for words based on their meaning, such as the names of people, locations, etc. NER is useful in extracting information along with classifying and categorizing them.

Semantic annotation

Semantic annotation adds metadata, additional information, or tags to text that involves concepts and entities, such as people, places, or topics, as we saw earlier.

Automated data annotation vs. human annotations.

As the hours pass by, human annotators get tired and less focused, which often leads to poor performance and errors. Data annotation is a task that demands utter focus and skilled personnel, and manual annotation makes the process both time-consuming and expensive.

That's why leading ML teams bet on automated data labeling.

Here's how it works—

Once the annotation task is specified, a trained machine learning model can be applied to a set of unlabeled data. The model will then be able to predict the appropriate labels for the new and unseen dataset.

Here's how you can create an automated workflow in V7.

However, in cases where the model fails to label correctly, humans can intervene, review, and correct the mislabelled data. The corrected and reviewed data can be then used to train the labeling model once again.

Automated data labeling can save you tons of money and time, but it can lack accuracy. In contrast, human annotation can be much more costly, but it tends to be more accurate.

Finally, let me show you how you can take your data annotation to another level with V7 and start building robust computer vision models today.

To get started, go ahead and sign up for your 14-day free trial.

Once you are logged in, here's what to do next.

1. Collect and prepare training data

First and foremost, you need to collect the data you want to work with. Make sure that you access quality data to avoid issues with training your models.

Feel free to check out public datasets that you can find here:

  • 65+ Best Free Datasets for Machine Learning
  • 20+ Open Source Computer Vision Datasets

Training data collection

Once the data is downloaded, separate training data from the testing data . Also, make sure that your training data is varied, as it will enable the learning algorithm to extract rich information and avoid overfitting and underfitting.

2. Upload data to V7

Once the data is ready, you can upload it in bulk. Here's how:

1. Go to the Datasets tab in V7's dashboard, and click on “+ New Dataset”.

New dataset creation in V7

2. Give a name to the dataset that you want to upload.

Naming new dataset in V7

It's worth mentioning that V7 offers three ways of uploading data to their server.

One is the conventional method of dragging and dropping the desired photos or folder to the interface. Another one is uploading by browsing in your local system. And the third one is by using the command line (CLI SDK) to directly upload the desired folder into the server.

Once the data has been uploaded, you can add your classes. This is especially helpful if you are outsourcing your data annotation or collaborating with a team, as it allows you to create annotation checklist and guidelines.

If you are annotating yourself, you can skip this part and add classes on the go later on in the "Classes" section or directly from the annotated image.

Data import in V7

💡 Pro tip: Not sure what kind of model you want to build? Check out 15+ Top Computer Vision Project Ideas for Beginners.

3. Decide on the annotation type

If you have followed the steps above and decided to “Add New Class”, then you will have to add the class name and choose the annotation type for the class or the label that you want to add.

Class creation

As mentioned before, V7 offers a wide variety of annotation tools , including:

  • Auto-annotation
  • Keypoint skeleton

Once you have added the name of your class, the system will save it for the whole dataset.

Image annotation experience in V7 is very smooth.

In fact, don't believe just me—here's what one of our users said in his G2 review:

V7 gives fast and intelligent auto-annotation experience. It's easy to use. UI is really interactive.

Apart from a wide range of available annotation tools, V7 also comes equipped with advanced dataset management features that will help you organize and manage your data from one place.

And let's not forget about V7's Neural Networks that allow you to train instance segmentation, image classification , and text recognition models.

Unlike other annotation tools, V7 allows you to annotate your data as a video rather than individual images.

You can upload your videos in any format, add and interpolate your annotations, create keyframes and sub annotations, and export your data in a few clicks!

Uploading and annotating videos is as simple as annotating images.

V7 offers frame by frame annotation method where you can essentially create a bounding box or semantic segmentation per-frame basis.

Annotating videos frame-by-frame in V7 and labels stacking

Apart from image and video annotation , V7 provides text annotation as well. Users can take advantage of the Text Scanner model that can automatically read the text in the images.

To get started, just go to the Neural Networks tab and run the Text Scanner model.

data annotation tools list

Once you have turned it on you can go back to the dataset tab and load the dataset. It is the same process as before.

Now you can create a new bounding box class. The bounding box will detect text in the image. You can specify the subtype as Text in the Classes page of your dataset.

data annotation tools list

Once the data is added and the annotation type is defined you can then add the Text Scanner model to your workflow under the Settings page of your dataset.

data annotation tools list

After adding the model to your workflow map your new text class.

data annotation tools list

Now, go back to the dataset tab and send your data the text scanner model by clicking on ‘Advance 1 Stage’; this will start the training process.

data annotation tools list

Once the training is over the model will detect and read text on any kind of image, whether it's a document, photo, or video.

data annotation tools list

💡 Pro tip: If you are looking for a free image annotation tool, check out The Complete Guide to CVAT—Pros & Cons

Data annotation: next steps.

Nice job! You've made it that far 😉

By now, you should have a pretty good idea of what is data annotation and how you can annotate data for machine learning.

We've covered image, video, and text annotation, which are used in training computer vision models. If you want to apply your new skills, go ahead, pick a project, sign up to V7, collect some data, and start labeling it to build image classifier or object detectors!

💡 To learn more, go ahead and check out:

An Introductory Guide to Quality Training Data for Machine Learning

Simple Guide to Data Preprocessing in Machine Learning

Data Cleaning Checklist: How to Prepare Your Machine Learning Data

3 Signs You Are Ready to Annotate Data for Machine Learning

The Beginner’s Guide to Contrastive Learning

9 Reinforcement Learning Real-Life Applications

Mean Average Precision (mAP) Explained: Everything You Need to Know

A Step-by-Step Guide to Text Annotation [+Free OCR Tool]

The Essential Guide to Data Augmentation in Deep Learning

data annotation tools list

Nilesh Barla is the founder of PerceptronAI, which aims to provide solutions in medical and material science through deep learning algorithms. He studied metallurgical and materials engineering at the National Institute of Technology Trichy, India, and enjoys researching new trends and algorithms in deep learning.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

Building AI products? This guide breaks down the A to Z of delivering an AI success story.

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Open Source Data Annotation Tools: 14 Best Tools

Open Source Data Annotation Tools - Featured Image | DSH

In the rapidly evolving field of artificial intelligence and machine learning, quality data is the lifeblood that fuels innovation. An essential step in the data preparation process is data annotation – labeling or tagging data to make it understandable for machine learning algorithms. Open source data annotation tools are pivotal in this process, empowering researchers and developers to efficiently label and organize data for various applications, from computer vision to natural language processing.

Table of Contents

What Is Data Annotation?

Data annotation adds metadata or labels to data to provide context and meaning. This labeling helps machine learning algorithms understand and interpret the data, ultimately enabling them to make accurate predictions or classifications. Data annotation can take various forms, including image tagging, text categorization, sentiment analysis labeling, and more.

Why is Data Annotation Important?

Data annotation is a crucial step in the data preparation process for machine learning and artificial intelligence applications. It serves several essential purposes:

  • Training Machine Learning Models: Annotated data is used to train machine learning algorithms. Labeling data with attributes or categories provides the necessary information for models to learn patterns and accurately predict.
  • Quality Control: Annotation ensures the quality of your data. It helps identify errors or inconsistencies in the dataset, enabling you to clean and refine it for better model performance.
  • Contextual Understanding: Annotations add context to data. They describe the content and meaning of data points, making it easier for algorithms to understand and interpret them correctly.
  • Evaluation: Annotated data is essential for evaluating the performance of machine learning models. Without labeled ground truth data, it’s challenging to measure the accuracy and effectiveness of your models.
  • Semantic Enrichment: Annotations enrich data semantically. For example, objects or regions of interest are labeled in image annotation, allowing algorithms to recognize specific objects within images.
  • Enhanced Decision-Making: In applications such as natural language processing (NLP) and sentiment analysis, annotations help in sentiment labeling, enabling businesses to make data-driven decisions based on customer feedback and sentiment analysis.
  • Automation and Efficiency: Data annotation is critical in creating training datasets for automated systems. Self-driving cars, for instance, rely on annotated data for recognizing road signs, pedestrians, and other vehicles.
  • Customization: Annotations can be customized to suit the needs of specific projects. Whether you’re working on object detection, named entity recognition, or image segmentation, annotations can be tailored to your requirements.

In summary, data annotation is the bridge that connects raw data to machine learning algorithms. It transforms data into a format that machines can comprehend, enabling them to learn, make decisions, and perform tasks accurately.

What Are Open Source Data Annotation Tools?

Open source data annotation tools offer a collaborative platform for annotators and data scientists to label data efficiently. These tools provide features such as image annotation, text annotation, video annotation, and annotation management.

Let’s explore some of the best open source data annotation tools and their standout features, advantages, and limitations.

14 Best Open Source Data Annotation Tools

  • Open Source Data Annotation Tools #1 LabelImg
  • Open Source Data Annotation Tools #2 RectLabel
  • Open Source Data Annotation Tools #3 VGG Image Annotator (VIA)
  • Open Source Data Annotation Tools #4 Labelbox
  • Open Source Data Annotation Tools #5 Supervisely
  • Open Source Data Annotation Tools #6 Annotator
  • Open Source Data Annotation Tools #7 CVAT (Computer Vision Annotation Tool)
  • Open Source Data Annotation Tools #8 LabelMe
  • Open Source Data Annotation Tools #9 Sloth
  • Open Source Data Annotation Tools #10 TagTog
  • Open Source Data Annotation Tools #11 ImageTagger
  • Open Source Data Annotation Tools #12 GATE
  • Open Source Data Annotation Tools #13 Siafoo
  • Open Source Data Annotation Tools #14 doccano

#1. LabelImg

LabelImg is a widely used open-source graphical image annotation tool. It allows users to draw bounding boxes around objects in images to create labeled datasets for object detection and computer vision projects.

  • User-friendly graphical interface.
  • Supports multiple annotation formats.
  • Customizable keyboard shortcuts.
  • Intuitive and easy to use.
  • Cross-platform compatibility.
  • Export annotations in various formats.
  • Limited support for complex annotations.
  • Lacks collaboration features.

#2. RectLabel

RectLabel is an image annotation tool specifically designed for Mac users. It simplifies the annotation process with features like automatic object recognition and tagging.

  • Intuitive drag-and-drop interface.
  • Automated object recognition.
  • Supports exporting to popular formats.
  • Mac-friendly design and features.
  • Speeds up annotation with object recognition.
  • Multiple export options.
  • Limited to macOS.
  • The paid version offers more advanced features.

#3. VGG Image Annotator (VIA)

The VGG Image Annotator (VIA) is an open-source image annotation tool developed by the Visual Geometry Group at the University of Oxford. It supports various annotation types, making it versatile for different projects.

  • Support for image, video, and audio annotations.
  • Annotation data is stored in a JSON file.
  • Customizable attributes for annotations.
  • Versatile annotation tool.
  • Allows collaboration on annotation projects.
  • Active development and community.
  • Learning curve for new users.
  • It may require some technical expertise.

#4. Labelbox

Labelbox is a comprehensive data annotation platform that offers both open-source and commercial versions. It provides tools for image, video, text annotation, and collaboration features.

  • Versatile annotation tools.
  • Collaboration and team management.
  • Data versioning and labeling workflows.
  • Supports multiple data types.
  • Collaboration features enhance productivity.
  • Scalable for large-scale annotation tasks.
  • The commercial version offers advanced features.
  • It may be overkill for small projects.

#5. Supervisely

Supervisely is an open-source platform designed for computer vision projects. It offers annotation tools, model training, and deployment features for building AI applications.

  • Image and video annotation.
  • Model training and deployment.
  • Collaboration and project management.
  • All-in-one platform for computer vision.
  • Supports deep learning model integration.
  • Active community and resources.
  • It may require familiarity with computer vision concepts.
  • Some features are more suitable for advanced users.

#6. Annotator

Annotator is a JavaScript library for building annotation tools. While not a standalone tool, it provides the foundation for creating custom annotation solutions.

  • JavaScript library for annotation.
  • Customizable annotation interfaces.
  • Integration with web applications.
  • Flexibility to create custom annotation tools.
  • Integration into existing web projects.
  • Open source and customizable.
  • Requires development skills to implement.
  • Not a standalone tool but a library.

#7. CVAT (Computer Vision Annotation Tool)

CVAT, or Computer Vision Annotation Tool, is an open-source platform designed for annotating images and videos for computer vision tasks. It offers collaboration features and supports multiple annotation formats.

  • Collaboration and user management.
  • A comprehensive tool for computer vision projects.
  • Collaboration features enhance teamwork.
  • Supports popular annotation formats.
  • It may require server setup and maintenance.

#8. LabelMe

LabelMe is an open-source image annotation tool developed by MIT. It allows users to annotate images with polygons, points, and lines, making it useful for various computer vision tasks.

  • Polygon, point, and line annotations.
  • Online annotation and storage.
  • Export annotations in XML format.
  • Customizable annotation shapes.
  • Web-based for easy access.
  • XML export for compatibility.
  • Limited to image annotation.
  • It may require hosting for collaborative projects.

Sloth is an open-source image annotation tool for macOS. It is designed for object detection and allows users to draw bounding boxes around objects in images.

  • Simple interface for object detection.
  • Export annotations in JSON format.
  • Thumbnail view for quick navigation.
  • Easy-to-use macOS application.
  • Lightweight and straightforward.
  • JSON export for compatibility.
  • Basic features compared to some other tools.

#10. TagTog

TagTog is an open-source text annotation tool focusing on natural language processing (NLP) projects. It allows users to annotate and tag text data for NLP model training.

  • Text annotation for NLP.
  • Collaboration features.
  • Support for various annotation types.
  • Tailored for NLP annotation.
  • Collaboration capabilities.
  • Multiple annotation types are supported.
  • Limited to text annotation.

#11. ImageTagger

ImageTagger is an open-source image annotation tool emphasizing simplicity and ease of use. It offers basic annotation features for labeling images.

  • Simple and user-friendly interface.
  • Support for image annotation.
  • Extremely user-friendly.
  • Quick and straightforward image labeling.
  • Export options for annotations.
  • Limited to basic image annotation.
  • It may not suit complex projects.

#12. GATE (General Architecture for Text Engineering)

GATE, which stands for General Architecture for Text Engineering, is an open-source software framework for NLP and text processing. While it is not a dedicated annotation tool, it offers powerful text-processing capabilities.

  • Text processing and annotation framework.
  • Supports NLP tasks.
  • Extensive plugin ecosystem.
  • Versatile for text processing projects.
  • Active community and plugin support.
  • Extensible and customizable.
  • It may require expertise in text engineering.
  • It’s not a dedicated annotation tool.

#13. Siafoo

Siafoo is an online platform with text and code annotation tools. It allows users to annotate code snippets and text data for various purposes.

  • Annotate code and text.
  • Collaboration on annotation projects.
  • Export annotated data.
  • A unique platform for code annotation.
  • Export options available.
  • Limited to code and text annotation.
  • Online platforms may have usage limitations.

#14. doccano

doccano is an open-source text annotation tool focusing on document classification and sequence labeling. It provides a web-based interface for annotating text data.

  • Document classification and sequence labeling.
  • Collaborative annotation projects.
  • Easy-to-use web interface.
  • Tailored for document classification.
  • Web-based and user-friendly.
  • It may require server setup.

How Do You Choose the Best Open Source Data Annotation Tool?

Selecting the right open-source data annotation tool is crucial for the success of your machine learning or AI project. Here are vital factors to consider when making your choice:

  • Annotation Type: Determine the type of annotation you need. Some tools specialize in image annotation, while others focus on text or video annotation. Choose a tool that aligns with your data and project requirements.
  • Ease of Use: Look for tools with user-friendly interfaces. Complex tools may require extensive training, while simpler ones can accelerate annotation.
  • Annotation Formats: Check if the tool supports common annotation formats, such as COCO, PASCAL VOC, or JSON. Compatibility with standard formats eases data integration with machine learning frameworks.
  • Collaboration Features: Collaboration features are essential for working with a team. Ensure the tool supports multiple users, allows version control, and offers communication within the platform.
  • Export Options: Consider the export options available. Tools that allow you to export annotated data in various formats offer greater flexibility for integration with your machine-learning pipeline.
  • Customization: Depending on your project’s unique requirements, customization may be important. Choose a tool that allows you to define custom annotation categories and attributes.
  • Community and Support: Assess the tool’s community and support resources. Active communities often provide valuable insights, tutorials, and troubleshooting assistance.
  • Scalability: If you anticipate working with large datasets, choose a tool that can handle scalability. Some projects may start small but grow rapidly, requiring an adaptable tool.
  • Cost: Consider your budget constraints. While many open-source tools are free, some offer paid versions with advanced features. Evaluate whether the free version meets your needs or if investing in a paid version is beneficial.
  • Documentation: Comprehensive documentation is invaluable. It helps users understand the tool’s features, functionalities, and how to troubleshoot common issues.
  • Security: Ensure the tool follows best practices, especially when working with sensitive or private data.
  • Community Feedback: Read reviews and feedback from other users. Their experiences can provide insights into the tool’s strengths and weaknesses.

Pros and Cons of Open Source Data Annotation Tools

Open Source Data Annotation Tools can be valuable assets for organizations and individuals involved in data labeling and annotation tasks. These tools have their own set of advantages and disadvantages. Here’s an overview:

  • Cost-Efficiency: Open Source Data Annotation Tools are typically free to use, reducing the financial burden associated with data labeling and annotation and making them cost-effective.
  • Customization: These tools often provide a high degree of customization, allowing users to tailor annotation workflows and formats to their specific data types and project requirements.
  • Community Collaboration: Many open-source projects related to data annotation have active communities of developers and users who collaborate to enhance tool functionality, share labeling guidelines, and troubleshoot issues.
  • Transparency: Open Source Data Annotation Tools are transparent in their source code, enabling users to inspect and modify the annotation process, which can be essential for ensuring data accuracy and reliability.
  • No Vendor Lock-In: With open-source tools, there is no vendor lock-in, allowing users to choose, modify, and maintain their annotation solutions independently.
  • Continuous Improvement: Open-source data annotation projects often benefit from ongoing contributions and updates from the community, ensuring that the tools evolve to meet changing data labeling requirements.
  • Complexity: Some Open Source Data Annotation Tools can be complex to set up and use, requiring users to have a certain level of technical expertise, which might be a barrier for non-technical users.
  • Limited Documentation: While many open-source data annotation projects provide documentation, some may lack detailed guides, making it challenging for beginners to use them effectively.
  • Support Variability: Although there is often a community of users willing to provide support, the quality and responsiveness of this support can vary, and users may not receive the same level of support as paid annotation tools.
  • Maintenance Burden: Users of Open Source Data Annotation Tools are responsible for maintaining and updating the software, which can be resource-intensive for organizations with limited technical resources.
  • Security Risks: Open source exposes Open Source Data Annotation Tools to potential security vulnerabilities if not regularly updated and patched, necessitating proactive security monitoring.
  • Integration Challenges: Some Open Source Data Annotation Tools may not have built-in integrations with other software and systems, requiring additional development efforts for seamless integration into data workflows.

In conclusion, Open Source Data Annotation Tools offer many advantages, including cost-efficiency, customization, and community collaboration. However, they also come with complexity, support, and maintenance challenges. The decision to use Open Source Data Annotation Tools or proprietary solutions depends on your specific data labeling needs, available resources, and technical capabilities. Carefully evaluating these factors is essential before choosing the right tool for annotation tasks.

Open source data annotation tools empower data scientists, researchers, and developers to prepare high-quality datasets for machine learning and artificial intelligence projects. These tools, ranging from image annotation to text labeling, are pivotal in accelerating the development and deployment of AI models.

Choosing the right tool requires careful consideration of annotation type, ease of use, collaboration features, export options, customization, and scalability. Additionally, community support and documentation are essential for a seamless annotation process.

Selecting the best open-source data annotation tool for your specific project needs sets the stage for successful model training, accurate predictions, and data-driven insights. Data annotation transforms raw data into a valuable asset, unlocking the potential for groundbreaking AI applications across various domains.

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Deep Learning Guide: Choosing Your Data Annotation Tool

We all know what data annotation is. It’s a part of any supervised deep learning project, including computer vision. A common computer vision task, like image classification, object detection, and segmentation requires annotations for each and every image fed into the model training algorithm. 

You simply must get a good tool for image annotation. In this article, we’ll be checking out a few top picks that I’ve worked with throughout my career as a deep learning engineer. Even though they have the same end goal, each annotation tool is quite unique and has individual pros and cons. 

To compare them, let’s define a list of criteria that will help you choose a tool that works best for you, your team, and your project.

How to choose the right data annotation tool?

The criteria for choosing the right data annotation tool are as follows:

  • Efficiency,
  • Functionality,
  • Formatting,
  • Application,

There are a lot of images available to deep learning engineers nowadays. Annotations are manual by nature, so image labeling might eat up a big chunk of time and resources. Look for tools that make manual annotation as time-efficient as possible. Things like convenient user interface (UI), hotkey support, and other features that save our time and improve annotation quality. That’s what efficiency is about.

Functionality

Labels in computer vision can differ depending on the task you’re working on. In classification, for example, we need a single label (usually an integer number) that explicitly defines a class for a given image. 

Object detection is a more advanced task in computer vision. In terms of annotations, for each and every object you need a class label, and a set of coordinates for a bounding box that explicitly states where a given object is located within an image. 

Semantic segmentation requires a class label and a pixel-level mask with an outline of an object. 

So, depending on the problem you’re working on, you should have an annotation tool that provides all the functionality you need. As a rule of thumb, it’s great to have a tool that can annotate images for all kinds of computer vision tasks you might encounter.

Annotations come in different formats: COCO JSONs, Pascal VOC XMLs, TFRecords, text files (csv, txt), image masks, and many others. We can always convert annotations from one format to another, but having a tool that can directly output annotations in your target format is a great way to simplify your data preparation workflow, and free up a lot of time.

Data annotation tools

Application

Are you looking for a web-based annotation app? Maybe you sometimes work offline, but still need to do annotations, and would like a window app that can be used online and offline? These might be important questions in the context of your project.

Some tools support both window apps and web-based apps. Others might be web-based only, so you won’t be able to use them outside of a web browser window. Keep that in mind when looking for your annotation tool.

If you work with sensitive data, consider privacy issues: uploading your data to a 3rd-party web app increases the risk of a data breach. Will you take the risk, or go with a safer, local annotator? 

Price always matters. From my personal experience, most engineers in small / medium size teams tend to look for free tools, and that’s what we’ll focus on in this article. 

For a fair comparison, we’ll take a look at paid solutions too, to figure out if they’re worth it. We’ll look at the circumstances when paid solutions make sense, and actually generate additional value.

Overview of labeling tools

You won’t see “best” or “worst” in my reviews of each annotation tool. For each of us, the “best” tool is one that meets our individual requirements and circumstances.

I will describe top 5 annotation tools , hopefully you’ll be able to choose one for yourself. These tools have proven to have good performance, and they’re well known among deep learning engineers. I’ve had a chance to work with each of these tools, and I’m happy to share my experience with you. Let’s jump in!

LabelImg is a free, open-source annotator. It has a Qt graphical interface, so you can install it and use it locally on any operating system . The interface is very simple and intuitive, so the learning curve won’t be extremely steep.

LabelImg can output annotations in multiple formats , including Pascal VOC XMLs and YOLO’s txts. It can also output CSVs and TFRecords with a few additional steps. 

LabelImg

LabelImg supports hotkeys to improve the annotation process and make it more convenient. Users can also enjoy a LabelImg image verification feature.

Annotation tools LabelImg

LabelImg has one, but very important drawback – it only supports bounding boxes for annotations . It’s also worth mentioning that LabelImg is strictly a window-based app, with no browser support. If these limitations are ok for you, LabelImg is indeed a good candidate for an annotator for your project. 

For more detailed review, guided installation and annotation process presentation, I recommend watching this tutorial created by The AI Guy .

VGG Image Annotator (VIA)

VIA is another tool for image annotations that should be on your watch list. It’s a free, open-source solution developed by a team from Oxford University.

In contrast to LabelImg, VGG Image Annotator runs entirely in a browser window . Even though it’s a web-based app, users can work offline in most web browsers. The app fits in a light-weight HTML page.

data annotation tools list

VIA has a broad range of functionality. You can draw different regional shapes around objects. Not just bounding boxes, VGG image annotator also supports circles, ellipses, polygons, points and polylines.

Annotation tools VGG

VIA can also annotate video frames, audio segments, and video subtitles . If you want a universal, but simple tool, VIA might be a good choice.

It has basic keyboard shortcuts that speed up the annotation process. I personally love how hotkeys work in VIA. It’s extremely convenient and well-organized.

Final annotation files can only be exported in a limited number of formats : COCO JSONs, Pascal VOC XMLs and CSVs are the supported formats. To cast annotations to other types of formats, additional external transformations will be needed, so consider that when making a decision.

To try VGG Image Annotator, check out demonstrations with preloaded data. Here are some use cases that you can browse through:

  • Basic image annotation example
  • Annotations for a video
  • Audio Annotation
  • Video subtitle annotation

If you’re curious how an annotation process is performed in VIA, this guided tutorial by BigParticle.Cloud will give you a good overview.

Computer Vision Annotation Tool (CVAT)

Annotation tools CVAT

CVAT’s user interface (UI) was optimized based on feedback from many professional annotation teams. Because of that, CVAT is very well designed for image and video annotation.

You can start an annotation job from CVAT’s website, and work fully online in a web-based application . CVAT’s website has some limitations, though:

  • You can only upload 500 mb of data,
  • Only 10 tasks per user.

Luckily, you can install it locally, and even your work offline . Installation is nicely documented , all operating systems are supported. 

Supported shape forms include rectangles, polygons, polylines, points and even cuboids, tags and tracks. Compared to the previous annotators, CVAT supports annotation for semantic segmentation . 

The amount of supported annotation formats for export is impressive. Here’s a complete list as of March, 2021:

  • Pascal VOC (xmls)
  • Segmentation masks for Pascal VOC
  • YOLO (txts)
  • MS COCO object detection (jsons)
  • LabelMe 3.0
  • Market-1501

Teams will find CVAT especially useful, because it’s so collaborative . CVAT lets users create annotation tasks and split up the work among other users. Moreover, annotation jobs can be monitored, visualized and analyzed using elasticsearch logstash kibana . It’s always great to have a chance of control over the labeling process, visualize progress, and manage it based on monitoring results.

data annotation tools list

Shortkeys cover most common actions and help a lot in real annotation work.

Annotation tools shortkeys

Automated annotation using pre-trained models is available. Users can select a model from a model zoo , or connect a custom model.

It has some flaws. Like limited browser support for CVAT’s client. It works well only in Google Chrome. CVAT wasn’t tested and optimized for other browsers. That’s why you can get unstable operations in other web browsers, although not always. I don’t use Google Chrome, and see no significant drops in performance, just some minor bugs that don’t bother me.

To get a sense of what CVAT is and its UI, you can try an online demo on CVAT’s website or watch a video of an object annotation process by Nikita Manovich .

Visual Object Tagging Tool (VoTT)

Annotation tools Vott

Microsoft has come up with its own solution for data annotation – Visual Object Tagging Tool (VoTT). Free, open-source tool with a very good reputation among data scientists and machine learning engineers.

Microsoft states that “VoTT helps facilitate an end to end machine learning pipeline”. It does with three main features:

  • Its ability to label images or video frames;
  • An extensible model for importing data from local or cloud storage providers;
  • An extensible model for exporting labeled data to local or cloud storage.

There’s both a web application and a native app. Compared to competitors, any modern web browser can run the annotator web app. It’s definitely a competitive advantage for those teams who got used to a particular browser, and don’t want to change it. 

On the other hand, VoTT’s web app is not as light-weight as VIA’s. It needs a bit of time and resources to be loaded in a browser window.

Annotation tools Vott app

Another drawback of VoTT’s web app – it can’t access the local file system. The dataset needs to be uploaded to a cloud, which can be inconvenient.

Visual Object Tagging Tool will ask you to specify two connections : for import (a source connection), and for export (a target connection). Projects in VoTT are designed as a labelling workflow setup , and require a source and target connections to be defined. You can analyze the way VoTT treats and organises labeling jobs in the official docs . The overall structure is very well designed and organized.

Annotation shapes in VoTT are limited to only two types: polygons and rectangles. However, the library of supported formats for export is quite rich. It includes:

  • Generic JSONs;
  • Pascal VOC;
  • Microsoft Cognitive Toolkit (CNTK);
  • Azure Custom Vision Service.

Annotation tools Vott web app

There are several keyboard shortcuts that let users always keep one hand on the mouse and one on a keyboard while annotating. The most common general shortcuts (copying, pasting, redoing) also have full support in VoTT.

To try Visual Object Tagging Tool, go to VoTT’s web app and give it a spin. Another great source of information about VoTT are guided tutorials. This tutorial by Intelec AI is one of my favourites. Consider watching it if you want to know more about VoTT, its UI and features.

Supervisely

I promised to put in some paid alternatives, and here it is. Supervisely – an end to end computer vision lifecycle platform. 

Supervisely is not just an annotation tool, it’s a platform for computer vision product development . Functionally, it’s not limited to a single data annotation process. Instead, teams and independent researchers, with or without machine learning expertise, can build deep learning solutions for their needs. All of that is done in a single environment.

data annotation tools list

In terms of labeling, Supervisely lets you annotate not only images and videos , but also 3D point clouds (3D scenes built by complex sensors like LIDARs and radar sensors), and volumetric slices.

Annotation tools include conventional points, lines, rectangles and polygons. Plus, some pixel level instruments: 

  • Brushes to draw any shape on a scene using a mouse hold;
  • Erasers that remove unwanted pixels.

Annotation tools Supervisely

Instance and semantic segmentation can be boosted up with one of the most prominent features of Supervisely. It’s called AI Assisted Labeling . You only have to define a shape of an instance, and a built-in neural net will do the rest of the job, filling up the target pixels.

Supervisely AI Assisted Labeling

Images are taken from AI Assisted Labeling web page

Annotation jobs can be managed at different scales . Depending on the team, different roles can be assigned to users. Labeling job progress is transparent and trackable.

Annotation tools Supervisely

Annotated data can be immediately used to train a neural net. You can select a model from a Model Zoo with pretrained models or go with a custom model of your choice. Either way will work.

Model Zoo is very rich with pretrained models. All models from the zoo can be added to an account and used to retrain a new custom model, so you don’t need to worry about the data format that a particular neural net requires. Supervisely does all data preparation and transformation steps for you. You’ll just have to fit in the data.

Trained models can be deployed as API. Alternatively, model weights and source code can be downloaded to use in any other scenario.

Supervisely has many other cool features, all of which I won’t be able to cover in this article, as we’re focusing on annotation tools. In case you’d like to know more about this platform, there’s an official youtube channel . I encourage you to browse through their playlists, and watch videos about topics, functionality and features that interest you. You also can look at some use cases if you wish.

In terms of pricing , students and fellow data scientists can use Supervisely at no costs. Companies and enterprises should get in touch to request pricing details . Supervisely states that their service is used by more than 25,000 companies and researchers worldwide, including big names like Mazda, Alibaba group, or Basf.

Conclusions

To choose a data annotator for a deep learning project, you need to be thorough: there are overwhelmingly many solutions available. Not surprisingly, each tool has different pros and cons. By now, you should have a good sense of how they differ, and what to look for depending on your needs.

We’ve gone over five candidates for consideration, looking at them from five different perspectives: efficiency, functionality, annotation formatting, application type and, of course, pricing.

LabelImg, our first candidate, is a simple and light weighted annotator. It’s extremely intuitive. If you don’t need unnecessary complexity, and solve object detection tasks with labeling, might be interested in using LabelImg. It will do exactly what you need.

VIA covers some of the drawbacks of LabelImg. You can use a web app, there’s a broader range of shapes for labeling; not just rectangles but also circles, ellipses, polygons, points and polylines.

CVAT, in contrast, supports semantic segmentation. Its collaborative functionality will serve as a good basis for effective team work.

VoTT is the only web-based annotator, optimized to work with every modern web browser. It’s backed by Microsoft, and simply can’t be a bad product.

Supervisely is the only paid candidate we’ve considered. Experienced deep learning engineers will definitely benefit from the automation and rich functionality of Supervisely. The less experienced will enjoy how it simplifies the machine learning workflow.

Find and select the tool that fits your requirements. I hope this article will help you make a good choice.

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Data Annotation – A Beginner’s Guide

Farooq Alvi February 21, 2024 Leave a Comment AI Careers Tags: data annotation 2024 Data Annotation tools what is Data Annotation

data annotation for beginners

At the heart of computer vision’s effectiveness is data annotation , a crucial process that involves labeling visual data to train machine learning models accurately. This foundational step ensures that computer vision systems can perform tasks with the precision and insight required in our increasingly automated world.

Data Annotation: The Backbone of Computer Vision Models

Data annotation serves as the cornerstone in the development of computer vision models, playing a critical role in their ability to accurately interpret and respond to the visual world. This process involves labeling or tagging visual data —such as images, videos, and also text—with descriptive or identifying information. By meticulously annotating data, we provide these models with the essential context needed to recognize patterns, objects, and scenarios.

This foundational step is similar to teaching a child to identify and name objects by pointing them out and naming them. Similarly, annotated data teaches computer vision models to understand what they ‘see’ in the data they process. Whether it’s identifying a pedestrian in a self-driving car’s path or detecting tumors in medical imaging, data annotation enables models to learn the vast visual cues present in our environment.

Understanding Data Annotation

The essence of data annotation.

In computer vision, data annotation is the process of identifying and labeling the content of images, videos, or other visual media to make the data understandable and usable by computer vision models. This meticulous process involves attaching meaningful information to the visual data, such as tags, labels, or coordinates, which describe the objects or features present within the data. Essentially, data annotation translates the complexity of the visual world into a language that machines can interpret, forming the foundation upon which these models learn and improve.

Types of Data Annotations in Computer Vision

The process of data annotation can take various forms, each suited to different requirements and outcomes in the field of computer vision. Here are some of the most common types:

Types of Data Annotations in Computer Vision

Image Labeling

Image labeling involves assigning a tag or label to an entire image to describe its overall content. This method is often used for categorization tasks, where the model learns to classify images based on the labels provided.

Bounding Boxes

Bounding boxes are rectangular labels that are drawn around objects within an image to specify their location and boundaries. This type of annotation is crucial for object detection models, enabling them to recognize and pinpoint objects in varied contexts.

Segmentation

Segmentation takes data annotation a step further by dividing an image into segments or pixels that belong to different objects or classes. There are two main types:

Semantic Segmentation: Labels every pixel in the image with a class of the object it belongs to, without distinguishing between individual objects of the same class.

Instance Segmentation: Similar to semantic segmentation but differentiates between individual objects of the same class, making it more detailed and complex.

Key Points and Landmarks

This annotation type involves marking specific points or landmarks on objects within an image. It’s particularly useful for applications requiring precise measurements or recognition of specific object features, such as facial recognition or pose estimation.

Lines and Splines

Used for annotating objects with clear shapes or paths, such as roads, boundaries, or even the edges of objects. This type of annotation is essential for models that need to understand object shapes or navigate environments.

Why Data Annotation Matters in Computer Vision

Ensuring quality and accuracy in data annotation.

Accurate annotations train models to understand subtle differences between objects, recognize objects in different contexts, and make reliable predictions or decisions based on visual inputs. Inaccuracies or inconsistencies in data annotation can lead to misinterpretations by the model, reducing its effectiveness and reliability in real-world applications.

The Cornerstone of Model Training

Data annotation is the foundation upon which their learning is built. Annotated data teaches these models to recognize and understand various patterns, shapes, and objects by providing them with examples to learn from. The quality of this teaching material directly influences the model’s performance—accurate annotations lead to more precise and reliable models, while poor annotations can hamper a model’s ability to make correct identifications or predictions.

Impact on Model Performance and Reliability

The performance and reliability of computer vision models are directly tied to the quality of the annotated data they are trained on. Models trained on well-annotated datasets are better equipped to handle the nuances and variability of real-world visual data, leading to higher accuracy and reliability in their output. This is crucial in applications such as medical diagnosis, autonomous driving, and surveillance .

Accelerating Innovation and Application

Quality data annotation also plays a vital role in driving innovation within the field of computer vision. By providing models with accurately annotated datasets, researchers and developers can push the boundaries of what computer vision can achieve, exploring new applications and improving existing technologies. Accurate data annotation enables the development of more sophisticated and capable models, fostering advancements in AI and machine learning that can transform industries and improve lives.

Challenges in Data Annotation

The process of data annotation, while crucial, comes with its set of challenges that can impact the efficiency, accuracy, and overall success of computer vision models. Understanding these challenges is essential for anyone involved in developing AI and machine learning technologies.

Scale and Complexity

One of the significant challenges in data annotation is managing the scale and complexity of the datasets required to train robust computer vision models. As the demand for sophisticated and versatile AI systems grows, so does the need for extensive, well-annotated datasets that cover a wide range of scenarios and variations. Annotating these large datasets is not only time-consuming but also requires a high level of precision to ensure the quality of the data. Additionally, the complexity of certain images, where objects may be occluded, partially visible, or presented in challenging lighting conditions, adds another layer of difficulty to the annotation process.

Subjectivity and Consistency

Data annotation often involves a degree of subjectivity, especially in tasks requiring the identification of nuanced or abstract features within an image. Different annotators may have varying interpretations of the same image, leading to inconsistencies in the data. These inconsistencies can affect the training of computer vision models, as they rely on consistent data to learn how to accurately recognize and interpret visual information. Ensuring consistency across large volumes of data , therefore, becomes a critical challenge, necessitating clear guidelines and quality control measures to maintain annotation accuracy.

Balancing Cost and Quality

The process of data annotation also presents a significant cost challenge, particularly when high levels of accuracy are required. Manual annotation , while offering the potential for high-quality data, is labor-intensive and costly. On the other hand, automated annotation tools can reduce costs and increase the speed of annotation but may not always achieve the same level of accuracy and detail as manual methods. Finding the right balance between cost and quality is a constant challenge for organizations and researchers in the field of computer vision. Investing in advanced annotation tools and techniques, or a combination of manual and automated processes, can help reduce these challenges, but requires careful consideration and planning to ensure the effectiveness of the resulting models.

Tools and Technologies in Data Annotation

A variety of tools and technologies that range from simple manual annotation software to sophisticated platforms offering semi-automated and fully automated annotation capabilities.

Manual Annotation Tools

Manual annotation tools are software applications that allow human annotators to label data by hand. These tools provide interfaces for tasks such as drawing bounding boxes, segmenting images, and labeling objects within images. Examples include:

LabelImg : An open-source graphical image annotation tool that supports labeling objects in images with bounding boxes.

VGG Image Annotator (VIA) : A simple, standalone tool designed for image annotation, supporting a variety of annotation types, including points, rectangles, circles, and polygons.

LabelMe: An online annotation tool that offers a web interface for image labeling, popular for tasks requiring detailed annotations, such as segmentation.

Your Image Alt Text

Semi-automated Annotation Tools

CVAT (Computer Vision Annotation Tool) : An open-source tool that offers automated annotation capabilities using pre-trained models to assist in the annotation process.

MakeSense.ai : A free online tool that provides semi-automated annotation features, streamlining the process for various types of data annotation.

Automated Annotation Tools

Fully automated annotation tools aim to eliminate the need for human intervention by using advanced AI models to generate annotations. While these tools can greatly accelerate the annotation process, their effectiveness is often dependent on the complexity of the task and the quality of the pre-existing data.

Examples include proprietary systems developed by AI research labs and companies, which are often tailored to specific use cases or datasets.

The Emergence of Advanced Annotation Platforms

Several commercial platforms have emerged that provide additional functionalities such as project management, quality control workflows, and integration with machine learning pipelines. Examples include:

Amazon Mechanical Turk (MTurk) : While not specifically designed for data annotation, MTurk is widely used for crowdsourcing annotation tasks, offering access to a large pool of human annotators.

Scale AI : Provides a data annotation platform that combines human workforces with AI to annotate data for various AI applications.

Labelbox : A data labeling platform that offers tools for creating and managing annotations at scale, supporting both manual and semi-automated annotation workflows.

Also Read: Computer Vision and Image Processing : Understanding the Distinction and Interconnection

Getting started with data annotation.

Here are some tips and recommendations to get you started:

Educate Yourself Through Online Tutorials

Several online platforms offer courses specifically designed to teach the fundamentals of computer vision and data annotation. These tutorials often start with the basics, making them ideal for beginners. 

Recommended tutorials:

CVAT – Nearly Everything You Need To Know

The Best Way to Annotate Images for Object Detection

Practice on Annotation Platforms

Hands-on experience is invaluable. Several platforms allow you to practice data annotation and even contribute to real-world projects:

LabelMe : A great tool for beginners to practice image annotation, offering a wide range of images and projects.

Zooniverse : A platform for citizen science projects, including those requiring image annotation. Participating in these projects can provide practical experience and contribute to scientific research.

MakeSense.ai : Offers a user-friendly interface for practicing different types of data annotation, with no setup required.

Label Studio : This is an open-source data labeling tool for labeling, annotating, and exploring many different data types.

Participate in Competitions and Open-Source Projects

Engaging with the community through competitions and open-source projects can accelerate your learning and provide valuable experience:

Kaggle : Known for its machine learning competitions, Kaggle also hosts datasets that require annotation. Participating in competitions or working on these datasets can offer hands-on experience with real-world data.

GitHub : Search for open-source computer vision projects that are looking for contributors. Contributing to these projects can provide practical experience and help you understand the challenges and solutions in data annotation.

CVPR and ICCV Challenges : These conferences often host challenges that involve data annotation and model training. Participating can offer insights into the latest research and methodologies in computer vision.

Also Read: Your 2024 Guide to becoming a Computer Vision Engineer

Data annotation is a critical yet underappreciated element in developing computer vision technologies. Through this article, we’ve explored the foundational role of data annotation, its various forms, its challenges, and the tools and techniques available to overcome these hurdles.

By understanding and contributing to this field, beginners can not only enhance their own skills but also play a part in shaping the future of technology.

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Data annotation: Everything about tools, use cases, and future directions [2024]

data annotation

When it comes to the global trend nowadays - artificial intelligence and machine learning, the first thing we care about is data. A machine learning model's life starts with data and ends with the deployed model, and turns out that high-quality training data is the backbone of a well-performing model.

Through this article, we'll examine what carries the core responsibility for ready-to-train data, also known as data annotation.

Data annotation is the action of adding meaningful and informative tags to a dataset, making it easier for machine learning algorithms to understand and process the data. Previously, data annotation was not as crucial as it is now for the reason that data scientists were using structured data which did not require many annotations. During the last 5-10 years, data annotation became more critical for machine learning systems so they can work effectively.

Without it, machine learning algorithms would be lost in a sea of unstructured data, struggling to distinguish one piece of information from another. Note that unstructured data makes up a big portion of data in the world – like emails, social media posts, image and audio data, text, sensor data, etc. – thus making the role of data annotation exceptionally important. We can make a bold statement and call data annotation an ingredient in the data processing cycle one can't afford to avoid. With the growing value of AI and machine learning and the exponentially growing amounts of data in the world, data annotation has become even more essential for businesses and organizations to stay competitive.

Whether you are new to data annotation or a seasoned professional, this article will provide valuable insights into the world of data annotation and help you stay on top of its latest trends.

data annotation types

Large language models (LLM) annotation

The end of 2022 marked the birth of what everyone's talking about now – ChatGPT and AI generated text. GPT and other large language models (LLMs) have come to reality due to a massive amount of labor done for annotation, and we're about to explore a few types of language models and their annotation procedures.

Encoder-decoder models

Back in 1997 Ramon Neco and Mikel Forcada suggested the “encoder-decoder” structure for machine translations, which became popular after 2016. Imagine translation is a text-to-text procedure, where you need techniques to first encode the input sentence to vector space, and then decode it to the translated sentence. This is the very simplified logic of encoder-decoder models.

encoder-decoder

Let's discuss the example of translation from English to French. The encoder logic is described in the first part of the above image. It takes the input sentence and converts it to some numerical representation that captures the inherent structure and patterns of that sentence.

encoder

The encoded information then passes to the decoder, which captures the information encrypted in the encoded sentence and generates translated output in French. Of course, there are much more complex processes happening under the hood, but for the sake of simplicity, we kept everything in a few sentences and basic terms.

decoder

Data annotation process here includes training data of pairs of sentences in different languages. Each pair will consist of an input sentence(in English) and an output sentence(in French). The source sentence serves as an input for the encoder, and the target is the output of the decoder. This is just the case of translation, and depending on the task, the annotation process will differ.

Popular encoder-based models in NLP include recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and more recently, transformer models like BERT (Bidirectional Encoder Representations from Transformers). While encoder-decoder first went viral in translation, they later emerged to tasks like text classification, sentiment analysis and text generation from prompts.

Transformer-based models

The problem with traditional encoder-decoder architectures lay in their sequential nature and their difficulty in capturing long-range dependencies in language. In the case of translation, for example, capturing the relationship between the first word and the last word in a long sentence becomes challenging for RNN-based models. This is what lead to the birth of transformers.

Transformer-based models were developed in 2017 by researchers in Google and came as a replacement for recurrent neural networks to cover up the areas that RNNs failed to succeed. They addressed such issues by introducing self-attention , enabling parallel processing and improving context understanding. The attention layer has access to all the previous states and weighs them according to a learned measure of relevance, providing relevant information about far-away tokens.

The end of 2017 marked the culmination of the recurrent networks era and models were already fully based on self-attention.

Let's break down the transformers lifecycle:

  • It all starts with basic language model training, which takes the majority of time for building transformer, since the model is being trained on a huge amount of text data.
  • After you have a good language model, it's time to finetune it. This involved training on a task-specific dataset with annotated training data.

Pre-training is typically done on a larger dataset than fine-tuning, due to the limited availability of labeled training data.

Reinforcement learning from human feedback (RLHF)

Reinforcement learning from human feedback (RLHF) is the practice of using human feedback and preferences in reinforcement learning tasks in order to optimize language models. By this, we aim to create a system that's able to quantify our preferences by assigning numerical rewards to language models' actions and trajectories. ChatGPT has been the greatest success of RLHF and takes responsibility for the current viral interest in RLHF; let's see what role data annotation takes in RLHF.

RLHF consists of the following phases:

  • Pre-training a language model (LM)
  • Training a reward model
  • Fine-tuning the LM with RL

The data annotation part is mainly involved in the second, training a reward model stage. Here, human annotators are ranking the results of LM, giving feedback in the simple form of yes/no approval; i.e. the language model comes up with responses and the human gives an opinion on which response of the agent is good enough to "deserve" a reward. It's important to note that the human annotation rewards have to be scalar so that our preferences are represented numerically.

data annotation tools list

LLMs in SuperAnnotate

At SuperAnnotate, we understand that every project has its unique data requirements. That's why our LLM tool is designed for customization, offering a flexible platform that adapts to the specific requirements of your project. Here's how we tailor our solution to fit your needs:

  • Customizable interface: Our platform's interface is fully adaptable, making it straightforward to compile data tailored to your unique case.
  • Expert team support: Scaling your project is made easy with our experienced team, ensuring efficient management for projects of any size.
  • Insightful analytics: Gain deeper insights into your data with our advanced analytics, helping to maintain high quality standards throughout your project.
  • Robust data security: We prioritize the security of your data with stringent data governance practices, giving you peace of mind.

API integration: If you have a custom API, integrating it with our platform is seamless, offering you greater control and versatility.

SuperAnnotate focuses on meeting the functional needs of our users, ensuring the success of every LLM project. Our platform provides a variety of templates for hands-on testing, including GPT fine-tuning ,  chat rating , using  RLHF for image generation ,  model comparison ,  video captioning ,  supervised fine-tuning , and more. If you've got a use case you want to explore, try the playground .

llms in superannotate

Image annotation

Let's start our data annotation journey with one of the most widely used processes in computer vision, image annotation . Image annotation is the action of tagging digital images with metadata or any additional information that helps to identify and understand the visual content.

image annotation

Breakdown of image annotation and its importance

You can refer to image annotation as the process of making an image easier to find. The best way to achieve this is by giving the image some sort of description, otherwise referred to as annotation. By giving the annotated, structured image dataset to our machine learning models, we allow them to train and deliver the desired results (this depends on the quality of the training data).

With the advent of computer vision and machine learning, image data annotation has become an essential ingredient of data annotation for many applications, including autonomous vehicles , agricultural automation systems , medical imaging , and surveillance systems .

Image annotation tools and use cases

We've touched upon the widespread usage of image data annotation across multiple industries, now it's time to take a closer look at each specific detail and practical application of the tool.

‍ Image classification (or tagging): Image classification is a fundamental data annotation process that involves assigning one or more labels to an entire image. With image classification, you aim to automatically identify the content of an image and categorize it accordingly. Say you're a farmer who wants to analyze crop health. By classifying your training data's crop images, the algorithms can detect early signs of disease or stress, enabling you to take preventative measures and increase your crop yields. ‍

Object detection: Object detection is the action of identifying and localizing objects within an image or a video. It is often confused with image classification, but there is a distinct difference between these two. Image classification refers to categorizing an entire image into one class. Whereas object detection localizes and categorizes objects in an image and assigns tags to each object.

A famous tool for object detection is bounding boxes. Bounding boxes are rectangles that surround an object of interest in an image with the purpose of providing a visual representation of the object's location, like locating pedestrians for autonomous vehicles, identifying people and objects in security camera footage, etc. Its technique is remarkable for its simplicity - it simply doesn't require a complex machine learning algorithm to train.

object classification and detection

‍ Image captioning (free text description): Image transcription is the process of extracting information from images. It's like making descriptive stories from images and keeping them in the form of textual annotated data. You need to give the tool images and data annotation requirements of the deliverable, and the tool will return the images together with the transcribed information. ‍

Optical character recognition: Optical character recognition (OCR) is a technology that allows computers to read and recognize text from scanned images or documents. This involves, for example, drawing bounding boxes around each line or block of text, which can be used to train OCR algorithms to recognize and extract the text accurately. Let's note that OCR's technology has revolutionized the way we interact with printed and handwritten text. How? It has enabled us to digitalize and preserve historical documents, automate data entry processes, and even enabled accessibility for people with visual impairments. OCR opportunities are diverse and endless, leaving us impatient to witness its future advancement.

Pose estimation (keypoint annotation): Pose estimation is the process of estimating the 2D or 3D coordinates of a human body in a given image or video. It involves detecting and tracking key points on the body and then using the information to determine the position and orientation of the body in 3D space. Key points usually correspond to joints, such as the shoulders, elbows, wrists, hips, knees, or other body parts. Applications of pose estimation in human health are widely recognized to analyze the movement of patients with neurological disorders, such as Parkinson's disease, strokes, and many other cases. It can track a patient's movement and analyze it in real-time, allowing for objective measurements of progress over time.

keypoint annotation

Instance segmentation (polygon annotation): Instance segmentation is a computer vision task that involves identifying and localizing each individual object instance within an image or video and assigning a unique label to each instance (polygon points). Imagine this as a more advanced form of object detection that not only identifies the box coordinates of the objects but also the exact pixel locations of the object.

Polygons is a famous tool for instance segmentation that creates ground truth data. It traces the outline of each object using a set of connected vertices, which define the shape and location of the object. The user typically selects a set of points along the boundary of the object using a polygon tool and then creates the annotated data. It usually takes a lot of time to trace the object which makes it very expensive to create large amounts of polygon datasets. To decrease the annotation time of these tedious tasks, researchers created several AI-assisted algorithms that help users create pixel-precise masks with a few button clicks. SuperAnnotate also integrates state-of-the-art algorithms providing the most accurate and intuitive tools for polygon annotation.

‍ Semantic segmentation: In computer vision, semantic segmentation is referred the AI or ML model which classifies each pixel in the image based on the predefined classes. Semantic annotation is the process of classifying each pixel and is used in many fields such as autonomous driving, retail, and fashion. Classifying each pixel with semantic annotation is probably the most tedious annotation work which makes it really hard for a data scientist to create well-performing semantic segmentation algorithms. To ease the pain of semantic annotation, companies like Segment and SuperAnnotate created the SuperPixel based approach which can accelerate the annotation process by several factors. The SuperPixel-based semantic annotation can be seen below.

Panoptic segmentation: Panoptic Segmentation combines semantic segmentation and instance segmentation into one algorithm. As a result, to annotate an image for panoptic segmentation one needs to use both techniques for semantic annotation and polygon annotation. ‍

Other use cases include rotated box annotation, lane annotation, cuboids, etc. There are many other data annotation practices that are often times used in niche markets. For example, rotated box annotation is quite similar to bounding box annotation, where one needs to specify the rotation angle in the bounding box. Cuboids are also very similar to bounding boxes in their concept, except they capture an object's depth in a 3d format. On the other hand, bounding boxes and lane annotations are very similar to polygon annotations. Such techniques are primarily used in the autonomous driving industry.

Image annotation with SuperAnnotate

SuperAnnotate offers a comprehensive set of tools for accurate and efficient image annotation for all the annotation tasks described above. The platform provides a wide range of user-friendly tools that make it easy to create accurate and precise data annotations. It also offers customization options, allowing users to create their own annotation templates and workflows. With the built-in quality control mechanisms as well as AI-assisted tools, SuperAnnotate makes sure that the annotations are up to their highest standards.  SuperAnnotate also provides secure and private data storage, guaranteeing that your data is safe and confidential.

Take a look at this annotation demo video with SuperAnnotate's platform:

Video annotation

Next in the list of commonly used data annotation types is video annotation . To summarize, video annotation is the action of detecting and classifying objects or actions within a video, which is also considered a more complex version of image annotation.

Introduction to video annotation and its importance

Since video data makes up a significant portion of media content, you can already guess the importance of its annotation practice. Let's dive deeper into the world of video annotation.

Video annotation tools and use cases

Video classification (or tagging): Video classification is the process of analyzing and categorizing video content into predefined classes or categories. In internet content moderation, video classification plays an important role in identifying and filtering out inappropriate, offensive, or harmful content, making sure that users have a safe and positive experience. ‍

Video captioning (free text description): Similar to image captioning, video captioning deals with extracting story and knowledge from video data and maintains the deliverable in textual form.

Video event or action detection: Video event or action detection is widely implemented in activity recognition and classification in sports videos, drawing a lot of attention from computer vision industry experts. Common applications range from the classification of different actions in sports videos such as a basketball player dribbling the ball or shooting a three-pointer, to performance analysis, athlete recruitment, fan engagement, and much more. Event detection is also widely used as an active learning step in video surveillance applications. In such applications, the events occur rarely, and finding potential annotation frames can be done by event annotation. ‍

Video object detection and tracking: Object detection in videos is the task of identifying the presence of an object in video frame sequences. Object tracking is monitoring an object's movement during a video sequence, including its presence, location, shape, size, etc. Here are some data annotation tools which are efficient for video tracking.

1. Tracking with bounding boxes is a fundamental technique in computer vision that involves detecting, localizing, and tracking objects within a sequence of video frames. Think about how vehicles can be detected and tracked in a traffic video. The process involves drawing bounding boxes around each vehicle and then generating a unique ID that can be used to track the same vehicle in the upcoming frames. Box tracking techniques are widely applied in industries such as autonomous driving, video surveillance, sports analytics, etc.

2. Polygon tracking, also known as video object segmentation, is similar to box tracking annotation but it tracks the exact object boundaries with precise polygons. The annotation is generally much more complex and can take longer if advanced automation tools are not used.

3. Keypoint annotation is used when the object shape itself is not our main concern, but we want to identify critical points within the shape of the object, track those points and know how they move or change their position. This technique is famous in human motion analysis, particularly in sport analytics applications.

Video annotation in SuperAnnotate

There are several factors that one needs to consider when choosing a video annotation tool. For example - for action detection and video captioning, smooth video playback in different speed options is very critical for an efficient annotation. On the contrary, when dealing with object tracking use cases with bounding boxes, polygons, or points supporting frame-based annotation and interpolation is becoming a key to speed up the annotation process. In more advanced use cases, AI-assisted labeling tools based on optical flow , video object tracking, or segmentation are becoming more essential to speed up annotation. SuperAnnotate's video annotation platform is made to speed up the annotation of all these use cases. Additionally, we created several tools to provide efficient collaboration and error detection, making the quality assurance process as important as the annotation process. Here is a small video snippet that demonstrates what the tool looks like.

Text annotation

It's time to learn about the language processing superhero in data annotation. Text annotation is the action of adding extra information to a text with the aim of helping machines understand human language. With text data annotation, machines are able to understand concepts and relationships within texts even if they're in an unclear form or language. Think of it as giving machines magic glasses to see through the complexity of human language.

data annotation tools list

Introduction to text annotation and its importance

Data annotation in text space is becoming more important than ever, especially with new applications created by ChatGPT or other large language models (LLMs) . However, before LLM use cases became popular, text annotation was still playing an integral role in extracting relevant data from various sources of text. In natural language processing (NLP), text annotation tasks are used for applications such as sentiment analysis , entity recognition, translation, and many more.

Text annotation tools and use cases

Text classification: Text classification is one of the most foundational tasks of NLP. Text classification algorithms analyze and recognize patterns within the text and accurately assign the to appropriate categories. These algorithms are instrumental in a wide array of applications, such as sentiment analysis, spam filtering, topic detection, and document organization.

Language translation: The name itself is pretty self-explanatory. Language translation is about using machine learning models to understand text data and translate them to another language. The process of using artificial neural network to predict the likelihood of a sequence of words in machine translation is called neural machine translation (NMT) . One of the most vivid applications of NMT is in communications, where AI translation can facilitate multilingual communication between individuals and groups that speak different languages. This can be particularly useful for businesses that operate globally, or for international conferences and events.

Named entity recognition (entity annotation): Named entity recognition is another text annotation technique that is used for unstructured data annotation. NER involves identifying and annotating data of named entities with specific categories. You can understand entities under the same category as words or phrases that explain similar concepts or mean the same thing. Take the sentence

"SuperAnnotate was ranked as the best data annotation platform in G2".

In this sentence, we can extract multiple entities: SuperAnnotate and G2 fall under "company" category, and "data annotation platform" belongs to a "product" entity. Scale this example and you will have a proper understanding of entity annotation!

named entity recognition text annotation

Coreference resolution (relationship annotation): Coreference resolution or relationship annotation is a text annotation task that identifies all phrases that refer to the same exact entity. To not confuse it with entity annotation, let's jump straight to a similar example.

"SuperAnnotate was ranked as the best data annotation platform in G2. The company received 92 reviews with 4.9/5 score in the world's largest software marketplace."

In this case, "the company" in the second sentence refers to "SuperAnnotate" , and "world's largest software marketplace" refers to "G2". With a text annotation tool, the annotated data will look the following way:

coreference resolution text annotation

Intent annotation: Intent annotation can be considered a subset of text classification but instead of predefined classes, one needs to classify based on the intent of the conversation's response (for example what your customers really want). Intent annotation is the ingredient for understanding the true purpose of text messages. By annotating each message with a specific intent category, such as "Booking Request" or "Complaint", you can unlock powerful insights into your customer's true needs and preferences.

Text annotation with SuperAnnotate

SuperAnnotate's text annotation tools are designed to be intuitive and easy to use, with features like keyboard shortcuts, auto-save, and collaborative annotation capabilities. Users can customize the annotation interface to annotate data based on their specific needs, and adjust settings such as font size, background color, and annotation type.

Audio annotation

Audio annotation has revolutionized the game of sound in the modern world. Every time we ask Shazam to find that sound we really like, ask Siri a question, or Spotify to recommend us a new song, we're using the benefits of audio data annotation. This technology allows us to categorize different types of sounds from voice assistants to wildlife monitoring.

Put on the headphones to crank up the volume and get into the world of "singing" data annotation.

audio annotation

Why annotate audio?

Audio data is generated every day, and without its annotation, large amounts of audio value would be lost. With audio annotations, we can train machines to recognize and categorize different types of audio data, from speech and music to ambient noise and animal sounds. With this powerful tool, we can unlock a world of possibilities for everything from speech-to-text transcription to music recommendation systems.

Audio annotation use cases

Audio classification: Audio classification is the data annotation process of classifying sound training data based on their characteristics. The objective of audio classification is to enable machines to identify and differentiate various types of audio, including music, speech, nature sounds, and many more. It's also widely used to classify music genres which helps companies like Spotify and others to recommend similar music based on its genre.

Audio transcription: Audio transcription is the simple process of converting spoken words of audio files into written text. A very useful practice of audio transcription is creating captions for audio and video materials such as interviews, films, or TV shows. Automating audio annotation is very essential for collecting high-quality training data. Whisper is a recent algorithm by OpenAI that helps transcribe audio files in different languages. The transcription is not always accurate when using such automated models, and to correct the initial model's predicted transcription one needs efficient audio annotation transcription tools. Luckily we have a great step-by-step  tutorial  on how to efficiently use Whisper prediction API and correct those predictions in SuperAnnotate’s audio annotation tool.

LiDAR annotation

As we covered image and video annotation, next comes LiDAR , a fancy abbreviation of light detection and ranging. Lidar is a remote sensing technology that uses laser pulses to measure distances between objects. LiDAR annotation has changed the game of data annotation and we're going to show you how.

what is lidar

What is LiDAR data and why annotate it?

The annotation techniques that we already discussed mostly covered detecting data in 2D space. However, let's not overlook the fact that we need a tool to calculate 3D information such as depth, the distance between objects, the reflectivity of the objects, and other cases where 2D techniques lack efficiency. LiDAR annotation addresses such issues and we're about to find out how.

To understand the most common application of LiDAR data annotation, let's first learn a new terminology: sensor fusion. Sensor fusion is the process of data collection from multiple sensors to create a more accurate and comprehensive understanding of the environment. In fact, information from just one source tends to be more biased and incomplete compared to combined annotated data from different sources. LiDAR is great for detecting the distance and position of objects in 3D space, but it can't always provide the full picture. That's where images come in, providing additional details such as color and texture.

LiDAR gained its popularity mainly after the recent hype around autonomous vehicles. As self-driving cars become more and more prevalent, LiDAR annotation emerges as a key technology that enables them to safely navigate their surrounding. Let's discuss the case of fusing LiDAR and images to create a more robust and accurate perception of autonomous vehicles.

LiDAR annotated data can provide accurate distance measurements for detecting obstacles and identifying road features. However, LiDAR data annotators alone cannot provide detailed information about the color, texture, and appearance of objects. By combining annotated images with LiDAR data, autonomous vehicles can extract additional information, such as object color and texture to facilitate their understanding of the environment.

LiDAR annotation use cases

LiDAR segmentation: Existing for the past 10 years, LiDAR technology has very recently become a hot topic, especially in LiDAR autonomous driving , due to its ability to deliver detailed 3D information about a vehicle's surroundings. This information includes obstacles, their position, the velocity with respect to the vehicle, and other data which is crucial for a safe driving experience. LiDAR segmentation tries to predict a point and labels it based on predefined labels. Accurate LiDAR segmentation algorithms allow autonomous systems to correctly identify all the obstacles and the road in the street thus making driving safer.

Object detection: Object detection with 3D bounding boxes for LiDAR data is the process of identifying and classifying objects in the point cloud data. Object detection in LiDAR data can be done much easier than segmentation and is often used for detecting pedestrians and cars for autonomous driving companies.

Sometimes the LiDAR data is collected as a sequence of frames. In such cases, Object Tracking becomes an important part of LiDAR annotation. Interpolation, AI-assisted labeling, and automated tracking algorithms are becoming essential in cases when one wants to perform fast and accurate annotation on LiDAR data.

Other types of data annotation

We already discussed the fundamental types of data annotation, but there are a few more that we shouldn't omit due to their widespread use in different industries. Let's explore a few of those data annotation methods.

PDF annotation

A lot of documents are kept in PDF format, making PDF annotation a necessity in financial, legal, and governmental organizations for digitalization purposes. PDF annotation is the action of adding notes, comments, or other metadata to a PDF document to provide additional information or feedback.

Website annotation

Website annotation is the process of adding notes or comments on a live website page, as well as classifying different websites based on predefined classes. It is often needed for content moderation for multiple purposes such as finding out whether the website is safe or not, or whether it contains any nudity, hate speech, etc.

Time series annotation

Time series data annotation involves annotating data that changes over time, such as sensor readings, stock prices, and ECG data. It is often used to predict abnormal activities and anomalies and the annotation tools help to identify and localize those events in the Times series data.

Medical data annotation

Medical data annotation involves annotating various medical images and records, such as X-rays, CT scans, and patient records. With relevant information, it becomes easier to develop accurate machine learning models for medical diagnosis and treatment.

Annotating any other data types with SuperAnnotate

With SuperAnnotate, you can bring your own data format and build a custom annotation editor that is best suited for your annotation needs. Our robust project management and data management toolset will be attached to the annotation editor that you will build and in turn enable the creation of high-quality training data at scale. As a part of the custom editor, we already released an HTML editor, a PDF annotation editor, and a Website annotation editor. You can read more about these editors in our documentation .

Final notes

In conclusion, data annotation plays an essential role in the success of supervised machine learning models as it provides accurately labeled datasets, and serves as the foundation for training data infrastructure. By employing robust annotation techniques for images, video, text, and audio files, machine learning engineers can ensure that their models effectively learn from high-quality annotations. These annotations become the new oil for companies trying to reach AI supremacy. We, at SuperAnnotate, are happy to fuel these companies to advance their AI capabilities through our platform and integrated services.

data annotation tools list

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data annotation tools list

data annotation tools list

21 best annotation tools for documents, video, and more

14 min read

December 4, 2023

Header_ Annotation and markup article (1)

In today’s data-driven world, annotation tools have become indispensable for various tasks, ranging from data labeling and content review to collaborative editing and research analysis. 

These tools empower users to add comments, highlights, and annotations to different types of content, enabling enhanced organization, collaboration, and understanding.

In this article, we will explore the top 21 annotation tools that cater to different purposes and domains.

What are annotation tools?

Annotation tools are software applications or platforms designed to facilitate the process of adding comments, highlights, or other annotations to various types of content. These tools play a crucial role in tasks such as data annotation, document review, collaborative editing, and research analysis. 

Annotation tools offer a range of features and functionalities that let users mark specific sections of text, images, videos, or documents for further context, explanation, or reference.

Supercharge your document review rounds

Get point-and-click feedback on your documents with Filestage.

Types of annotation tools

There are various different types of annotation tools on the market that cater to specific needs. 

Here’s a list of the seven most common types of annotation tools: 

  • Data annotation tools – these tools allow users to highlight, underline, strikethrough, or add comments to text-based content
  • Image annotation tools – annotate images by adding text, shapes, or drawings
  • Video annotation tools – annotate videos frame by frame, marking objects, actions, or events
  • Audio annotation tools – annotate audio files by adding markers, tags, or comments at specific timestamps or sections
  • Web annotation tools – annotate web pages or online documents, facilitating collaborative research and discussions
  • Document annotation tools – annotate academic papers, research, articles, and more
  • Code annotation tools – add comments, explanations, or documentation within source code files

Our shortlist of the top three annotation tools

Annotation tools can serve various different purposes. Here’s a closer look into the top three annotation tools that offer a comprehensive set of features from different fields of annotation.

1. Filestage – the best annotation tool for reviewing various file formats

Filestage is a review and feedback tool that makes it simple to collaborate on PDF documents, share videos , graphics, and marketing projects in general. 

It offers project managers a wide range of annotation and proofing features, which makes it easy for your internal and external key stakeholders to leave and discuss logical in-context feedback in real time.

A range of workflow features will make sure that you never miss a deadline or a game-changing suggestion. Next to custom pricing options, Filestage offers a Free plan – so you can check out the markup software at no risk.

And with the ability to integrate Filestage with Microsoft Teams, Jira, Adobe Premiere Pro, and a bunch of other tools, it fits in perfectly with your tool stack.

Key features

  • Collaborative video annotation – Filestage enables multiple stakeholders to review and annotate various file types, including images, videos , and documents, in a centralized platform
  • Review workflow management – It offers customizable workflows and task assignments, facilitating seamless collaboration and efficient annotation processes
  • Commenting and markup tools – Users can leave comments, draw annotations, and highlight specific areas within files to provide feedback and suggestions
  • Document version control – Filestage keeps track of file versions, allowing reviewers to compare changes and annotations over time
  • Approval and feedback management – It provides features for approving or rejecting annotations and consolidating feedback from multiple reviewers

2. Labelbox

labelbox

Labelbox is a popular data annotation platform that offers a wide range of annotation capabilities for images, videos, and text. It provides an intuitive interface for annotating and labeling data, along with powerful collaboration and project management features.

Labelbox supports various annotation types, including bounding boxes, polygons, keypoints, and semantic segmentation. It also offers automation features like active learning and model-assisted labeling.

  • Data annotation – Labelbox supports a range of annotation types, including bounding boxes, polygons, semantic segmentation, and keypoint annotations, for various data types such as images, videos, and text
  • Collaboration and review – It allows teams to collaborate on annotation projects, review annotations, and manage annotation quality control.
  • Automation and active learning – Labelbox offers features for automating certain annotation tasks using pre-trained models and implementing active learning techniques to reduce annotation efforts
  • Workflow management – users can create customized annotation workflows, assign tasks, and track the progress of annotation projects
  • Integration and deployment – Labelbox provides integration with popular machine learning frameworks and cloud storage platforms for seamless data management and model deployment

3. SuperAnnotate

SuperAnnotate

SuperAnnotate is a versatile and feature-rich data annotation platform that caters to image and video annotation needs. It offers a user-friendly interface with advanced annotation tools for bounding boxes, polygons, key points, and semantic segmentation.

SuperAnnotate provides powerful automation capabilities, including AI-assisted annotation and collaborative review workflows. It also offers integrations with popular machine-learning frameworks and platforms.

  • Advanced annotation tools – SuperAnnotate offers a wide range of annotation tools and techniques, including bounding boxes, polygons, keypoint detection, instance segmentation, and semantic segmentation
  • Collaboration and review – It enables real-time collaboration among team members, allowing them to review and validate annotations together
  • Quality control – SuperAnnotate provides tools for quality control, such as metrics calculation, consensus review, and inter-annotator agreement analysis
  • Automation and AI assistance – The platform uses AI to accelerate annotation tasks, improve annotation efficiency, and reduce human error
  • Project management and versioning – SuperAnnotate offers project management features, version control, and comparison tools for managing and tracking annotation projects

Best data annotation tools

Data annotation tools are software applications or platforms specifically designed to assist in the process of annotating or labeling data.

Here are the top two data annotation tools available on the market:

1. Amazon SageMaker Ground Truth

data annotation tools list

SageMaker Ground Truth is a data annotation tool provided by Amazon Web Services (AWS). It simplifies the process of creating high-quality labeled datasets for machine learning models. It offers a scalable and customizable platform for annotation tasks, supporting both automated and human-in-the-loop labeling.

SageMaker Ground Truth supports various annotation types, such as bounding boxes, semantic segmentation, and text classification. It integrates seamlessly with other AWS services, making it convenient for end-to-end machine learning workflows.

  • Customizable annotation workflows – It allows users to create custom annotation workflows tailored to specific data types and annotation tasks
  • Human-in-the-loop annotation – It combines automated annotation with human review, providing mechanisms to review and improve machine-generated annotations
  • Scalability – The tool is designed to handle large-scale annotation projects
  • Active learning – It supports active learning techniques to intelligently select samples for annotation and optimizes the annotation process
  • Integration with Amazon Services – It seamlessly integrates with other Amazon Web Services (AWS) products, such as Amazon Rekognition, for enhanced annotation capabilities

2. SuperAnnotate

SuperAnnotate is a versatile and feature-rich data annotation platform that caters to image and video annotation needs. It offers a user-friendly interface with advanced annotation tools for bounding boxes, polygons, keypoints, and semantic segmentation.

SuperAnnotate provides powerful automation capabilities, including AI-assisted annotation and collaborative review workflows. It also offers integrations with popular machine learning frameworks and platforms.

  • Advanced annotation tools
  • Collaboration and review
  • Quality control
  • Automation and AI assistance
  • Project management and versioning

Image annotation tools

Image annotation tools allow you to annotate images by adding bounding boxes, polygons, keypoints, or semantic segmentation masks to identify objects or regions of interest within the image.

Here are the two most popular image annotation tools:

1. RectLabel

data annotation tools list

RectLabel is a powerful visual object tagging tool specifically designed for object detection and localization tasks. It provides an intuitive interface with robust image annotations, including bounding boxes, polygons, lines, and keypoints.

RectLabel supports automatic annotation suggestions and offers features like image segmentation, instance tracking, and batch processing. It also integrates seamlessly with popular machine learning frameworks like TensorFlow and PyTorch.

  • Image annotation – RectLabel focuses on image annotation and offers tools for bounding box annotation, segmentation masks, keypoints, and text annotations
  • Object tracking – It provides features for object tracking within videos, allowing users to annotate objects across frames and track their movement
  • Collaboration and versioning – RectLabel supports collaboration among team members and keeps track of annotations and changes across versions of the data
  • Customizable annotation tools – Users can customize annotation tools and labels to match their specific annotation requirements and workflow
  • Export formats – RectLabel allows users to export annotations in various formats, including Pascal VOC, COCO, YOLO, and TensorFlow Object Detection API

2. VGG Image Annotator (VIA)

VGG-Image-Annotator-VIA-1

VIA is a versatile and open-source image annotation tool developed by the Visual Geometry Group at the University of Oxford. It supports annotation of various shapes, including points, polygons, rectangles, and ellipses.

VIA allows for annotating images individually or in batches, and it supports exporting annotations in multiple formats. It also offers customizability and extensibility, making it suitable for research and specialized annotation requirements.

  • Multi-format support – VIA supports a wide range of image formats, allowing users to annotate images in formats such as JPEG, PNG, and GIF
  • Multiple annotation types – It provides various annotation types, including bounding boxes, polygons, keypoints, and region descriptions, to annotate objects and regions of interest within images
  • Collaboration and version control – VIA supports collaborative annotation, allowing multiple users to work on the same set of images simultaneously
  • Annotation import/export – VIA allows users to import and export annotations in different formats, including JSON, CSV, and VIA project files
  • Customization and automation – VIA offers customization options, such as custom attribute definitions, and allows users to apply pre-trained models for automated annotation

Video annotation tools

As the name suggests, video annotation tools are used to add additional information on videos. This means that you can mark objects, track movements, or label frames.

Here are the two best annotation tools on the market:

1. Video Annotation Tool from Internet Curation (VATIC)

data annotation tools list

VATIC is a popular open-source video annotation tool developed by the Computer Vision Group at Stanford University. It offers an intuitive web-based interface for annotating videos frame by frame. Users can draw bounding boxes, track objects, and add labels or attributes to the annotated regions. VATIC supports collaboration, allowing multiple annotators to work on the same video simultaneously.

  • Video annotation capabilities – VATIC focuses specifically on video annotation, allowing users to annotate objects, track their movements, and draw on videos
  • Collaborative annotation – It supports collaborative annotation workflows, enabling multiple users to annotate videos and review annotations together.
  • Tracking and object identification – VATIC provides tools for object tracking, allowing users to annotate objects across frames and track their trajectories
  • Review and quality control – The tool offers review and validation features
  • Project management – VATIC includes project management features, such as task assignment and progress tracking

2. Computer Vision Annotation Tool (CVAT)

data annotation tools list

CVAT is a powerful open-source video annotation tool developed by Intel. It supports annotation for a wide range of tasks, including object detection, tracking, and segmentation. CVAT allows users to annotate videos by drawing bounding boxes, polygons, polylines, or points.

It offers collaborative annotation, synchronization across multiple annotators, and detailed annotation statistics. CVAT also provides automation features like interpolation, semi-automatic annotation, and inference integration.

  • Wide range of annotation types – CVAT supports diverse annotation types
  • Collaborative annotation and review – It enables real-time collaboration among team members, by collectively annotating and reviewing data
  • Automatic annotation – CVAT offers AI-based assistance to accelerate annotation tasks and reduce manual effort
  • Advanced video annotation features – CVAT provides comprehensive video annotation capabilities, including object tracking, video segmentation, and frame-level annotation
  • Integration and deployment –The tool allows integration with deep learning frameworks and supports exporting annotations in formats compatible with popular machine learning libraries

Audio annotation tools

When looking into different audio annotation tools, you should consider all the file formats it supports, and whether it’s designed for more complex annotation tasks.

Here are the top two audio annotation tools with comprehensive set of audio annotation features:

1. EUDICO Linguistic Annotator (ELAN)

data annotation tools list

ELAN is a powerful and widely used audio annotation tool developed by the Max Planck Institute for Psycholinguistics. It offers a comprehensive set of features for annotating audio and video recordings.

ELAN allows users to create and edit time-aligned annotations for multiple tiers, such as transcriptions, translations, and linguistic analysis. It supports various audio formats, offers advanced search and analysis capabilities, and is suitable for linguistic research and complex annotation tasks.

  • Linguistic annotation – ELAN is specifically designed for linguistic annotation, enabling users to annotate speech, language, and gesture data
  • Multi-layer Annotation – It supports multiple layers of annotation, allowing users to create and align annotations at different linguistic or temporal levels
  • Time-aligned annotations – ELAN provides precise time alignment for annotations
  • Metadata and transcription – The tool allows users to add metadata, transcriptions, and descriptive information to annotations, providing contextual information
  • Annotation visualization and analysis – ELAN offers visualization and analysis tools to explore and analyze linguistic annotations and their relationships

data annotation tools list

Praat is a versatile and popular tool for audio annotation and analysis. It provides a wide range of features for manipulating and annotating audio recordings. Praat allows users to create annotations, transcriptions, and phonetic labels at various time points.

It offers visualization tools, signal processing capabilities, and scripting options for advanced customization. Praat is widely used in phonetics, speech analysis, and linguistic research.

  • Speech and phonetics annotation – Praat is a specialized tool for speech and phonetics analysis, providing annotation features for audio recordings and linguistic analysis
  • Waveform and spectrogram visualization – It offers visualizations of audio waveforms and spectrograms to facilitate detailed analysis and annotation
  • Annotation types – Praat supports various annotation types, including segment labels, point labels, interval labels, and boundary labels, for precise annotation of speech data
  • Scripting and automation – The tool allows users to create scripts to automate repetitive annotation tasks or perform advanced analyses
  • Acoustic analysis – Praat allows users to extract and analyze acoustic features from speech data

3. Audacity

data annotation tools list

Although primarily known as an audio editing tool, Audacity also includes basic annotation features. It allows users to select specific regions of audio and add labels, comments, or markers.

While Audacity’s annotation capabilities may not be as extensive as dedicated annotation tools, it can be a convenient option for simple audio annotation tasks or when combined with its audio editing functionalities.

  • Audio recording and editing – Audacity allows users to record, import, and edit audio files with a range of editing capabilities, including cut, copy, paste, and apply effects
  • Multi-track editing – It supports multi-track editing, allowing users to work with multiple audio tracks simultaneously, adjust levels, and create complex audio compositions
  • Effects and plugins – audacity offers a wide range of built-in audio effects, such as reverb, equalization, and noise reduction
  • Spectrogram analysis – It provides a spectrogram view that displays the frequency and intensity of audio over time, allowing users to visualize and analyze audio characteristics
  • Cross-platform and open source – Audacity is a free and open-source tool available for Windows, macOS, and Linux, making it accessible to users across different operating systems

Web annotation tools

When it comes to annotating a web page, it’s best to turn to a specialized web annotation software.

Here are three great examples of web annotation tools:

1. Hypothesis

data annotation tools list

Hypothesis is a popular web annotation tool that allows users to annotate web pages and PDF documents. It offers a browser extension that enables users to highlight text, add comments, and create discussions within the context of the webpage. Hypothesis supports public and private annotations, collaboration features, and integration with learning management systems. It promotes open annotation standards and interoperability.

  • Web annotation – Hypothesis allows users to annotate web pages, documents, and digital content, making it easy to highlight, comment, and add notes directly on the content being viewed
  • Collaboration and discussion – It supports collaborative annotation, allowing users to engage in discussions, reply to annotations, and interact with other users’ annotations
  • Privacy and control – Hypothesis provides privacy controls, allowing users to choose whether their annotations are public or restricted to a specific group or audience
  • Integration with learning management systems (LMS) – It offers integration with popular LMS platforms, allowing educators to incorporate collaborative annotation activities into their online courses
  • API and customization – Hypothesis provides an API for developers to integrate annotation capabilities into their own applications or websites

data annotation tools list

Diigo is a versatile web annotation and bookmarking tool that enables users to highlight, bookmark, and add sticky notes to web pages. It offers browser extensions for easy annotation and organization of online content.

Diigo supports social annotation functionality, sharing and collaboration options, and the ability to create private or public annotation groups. It also provides a personal library for managing annotated resources.

  • Web highlighting and sticky notes – You can add sticky notes directly on web pages to mark important information and save personal notes
  • Webpage bookmarking and organization – It allows users to bookmark webpages and save them in an organized manner with tags, folders, and search functionality for easy retrieval
  • Social annotation and collaboration – Diigo supports social annotation, allowing users to share their highlights, annotations, and bookmarks with others
  • Webpage archiving – It provides a webpage archiving feature, allowing users to capture and save a copy of webpages, even if the original page is modified or removed
  • Knowledge sharing and discovery – Diigo offers a platform for users to discover and explore content shared by others, expanding their knowledge base and staying updated with new information

data annotation tools list

Kami is a web-based annotation tool designed for collaboration and document annotation. It supports annotating PDF files , Google Drive documents, and web pages. Kami allows users to highlight text, add comments, draw shapes, and insert images within documents.

It offers real-time collaboration, sharing options, and integration with Google Classroom and learning management systems.

  • PDF annotation and markup – Kami allows users to annotate and markup PDF documents with tools for highlighting, underlining, adding comments , and drawing shapes
  • Collaboration and real-time editing – It supports real-time collaboration, enabling multiple users to work on the same document simultaneously and view each other’s annotations in real time
  • Document organization and management – Kami provides features for organizing and managing PDF documents, including file merging, splitting, and rearranging pages
  • Integration with Google Drive and Classroom – It offers integrations that allow you to easy access your files and create streamlined collaboration within the Google ecosystem
  • Accessibility and integration with assistive technology – Kami offers accessibility features, including support for screen readers and integration with other tools to enhance the accessibility of PDF documents

Document annotation tools

Unlike any AI and natural language processing annotation tools, document annotation tools have a completely different purpose. They allow you to mark certain areas of your document and leave in-context feedback on top.

Here’s a look into some of the best document annotation tools:

1. Adobe Acrobat

adobe-acrobat-reader

Adobe Acrobat is a widely used and feature-rich document annotation tool. It offers a comprehensive set of annotation tools, including highlighting, underlining, strikethrough, adding comments, and drawing shapes. Adobe Acrobat allows for collaboration and review workflows, supports form filling, and offers advanced features like redaction and document security. It is compatible with PDF documents and is available for both desktop and mobile devices.

  • Comprehensive PDF editing features
  • PDF form creation and fillable forms
  • Document security and protection
  • OCR (Optical Character Recognition)
  • Integration with Adobe Creative Cloud

data annotation tools list

Xodo is a versatile and user-friendly document annotation tool available on various platforms, including Windows, Mac, Android, and iOS. It provides a range of annotation tools, such as highlighting, underlining, adding text, shapes, and signatures. Xodo offers seamless syncing across devices, supports collaboration, and allows for document signing. It is particularly popular for its smooth and responsive user interface.

  • PDF annotation and markup
  • Collaboration and syncing
  • Document organization and management
  • PDF form filling and signing
  • Cloud storage integration

3. Nitro PDF

data annotation tools list

Nitro PDF is a robust document annotation tool that offers a wide array of annotation options. It enables users to highlight text, add comments, draw shapes, and apply stamps or signatures.

Nitro PDF supports collaboration and version control, and it provides advanced features like OCR (Optical Character Recognition) and PDF form creation. It is available for both Windows and Mac platforms.

  • PDF creation and conversion
  • PDF editing and annotation
  • Collaboration and review features
  • Document signing and security
  • OCR capabilities to convert scanned documents into searchable and editable text

Code annotation tools

If you’re looking to review a code, then these are the tools for you. These tools offer features like code highlighting, commenting, and version control integration, making it easier to leave feedback and collaborate on source code.

Here are the two best code annotation software:

1. Visual Studio Code (VS Code)

data annotation tools list

VS Code is a popular and feature-rich code editor that offers built-in support for code annotation. It provides features like line commenting, block commenting, and the ability to add descriptive comments within code.

VS Code also supports extensions that enhance code annotation capabilities, such as adding tags, generating API documentation, or creating interactive code snippets.

  • Rich code editing experience with features like syntax highlighting, auto-completion, and intelligent code suggestions
  • Extensibility and customization options to tailor the environment to their specific needs
  • Version control integration with version control systems like Git, providing tools for managing code repositories, tracking changes, and resolving conflicts
  • Built-in debugging capabilities for various programming languages
  • It includes an integrated terminal for executing commands and a task runner for automating repetitive tasks within the development workflow

data annotation tools list

Eclipse is a widely used integrated development environment (IDE) that includes comprehensive code annotation features.

It supports commenting at the line, block, or method level and provides options to generate Javadoc comments for Java code. Eclipse also offers refactoring tools and code analysis features that aid in maintaining code quality and documentation.

  • Robust Java Development environment – Eclipse is renowned for its strong support for Java development, offering features like code completion, refactoring tools, and debugging capabilities
  • Plugin Ecosystem – It boasts a vast ecosystem of plugins and extensions, allowing users to enhance Eclipse’s functionality for various programming languages and development purposes
  • Project management and tools integration
  • Debugging and profiling capabilities – It allows users to identify and resolve issues in their code and optimize performance with powerful debugging capabilities
  • Collaboration and version control – Eclipse supports integration with version control systems like Git and provides features for collaborative development, code reviews, and team coordination

3. JetBrains IntelliJ IDEA

data annotation tools list

IntelliJ IDEA is a powerful IDE known for its extensive code annotation capabilities. It provides various features to add comments, generate Javadoc, and create code documentation. IntelliJ IDEA supports different programming languages, including Java, Kotlin, Python, and JavaScript.

It offers code inspections, auto-completion, and other intelligent features that facilitate effective code annotation.

  • Intelligent code editor with features like code completion, intelligent code analysis, and smart suggestions
  • Advanced refactoring tools that assist in restructuring code and improving its quality
  • Built-in version control with systems like Git, Mercurial, and Subversion
  • Productivity boosters, including features like code generation, live templates, quick-fix suggestions, and customizable code inspections to catch potential errors and improve coding speed
  • Robust plugin ecosystem

Annotate your files for free with Filestage

Filestage is a free annotation tool that allows you to seamlessly leave feedback on top of PDF documents, videos, graphics, and marketing projects in general. 

With Filestage, you can create a smooth annotation process by allowing all important stakeholders to leave feedback and review files.

Filestage’s free version is designed for freelancers and smaller teams and includes:

  • Unlimited files, versions, and reviewers
  • 2 active projects
  • 2 GB of storage
  • 2 review steps
  • Support for all file formats
  • Integrations
  • Due dates and reminders
  • Awaiting my review

Final thoughts

Various annotation tools are developed to cater to a wide range of purposes and industries. Whether you’re annotating images, videos, audio, code, or text content, these 21 annotation tools discussed in this article offer valuable features and capabilities to streamline your annotation workflows.If you’d like to see how Filestage can help you annotate various file formats and streamline your annotation process, make sure to try it for free →

Nika Prpic

Annotation and markup

data annotation tools list

11 best free PDF markup software on the market in 2024

How often did you want to highlight or leave notes on a text without having to print it? But as soon as you realized it was a PDF document, you knew it wouldn't be easy. Luckily, PDF markup tools are true lifesavers in this situation.

11 min read

data annotation tools list

How to annotate Microsoft Word documents (+ tips for accurate annotations)

Most of us would agree that Microsoft Word is kind of a legend in the world of text and editing tools. After all, it has been around since 1983.

data annotation tools list

How to annotate on Google Docs in seconds

Google Docs has become synonymous with effective online document collaboration. The tool has been changing the game in how we co-create and store documents in the cloud, which has won Google over two billion users.

data annotation tools list

How to annotate documents for fast and clear feedback

When it comes to giving feedback on a document, it can be tricky to explain what you mean using words alone. That’s where annotations come in.  Whether you’re reviewing a Word document, a PDF file, or any other type of document, using annotations to share your feedback makes the entire process clearer, easier, and even – dare I say it – more enjoyable.

Katie Garrett

data annotation tools list

How to annotate a website for fast feedback and auditing

There are around 1.13 billion websites on the internet in 2023. That’s more than three websites for every single person in the United States. But, the question is, how much of that is valuable? According to Forbes, 88% of online users won’t return to a site after a bad experience. And who can blame them? With so much out there to explore, people don’t have time or space for garbage.

data annotation tools list

How you can use PDF annotation to improve your document review process

As humans, we’re guilty of being creatures of habit. All too often, this shows itself in a tendency to stick to old-fashioned methods. Ditch the printer! In this article, we’ll be taking you through all the benefits of PDF annotation.

Muriel Skusa

data annotation tools list

How to annotate video: Your guide for creating great video content

It’s never been easier to make,  share, and upload videos online. You can do it. I can do it. Heck, my 80-year-old Grandma can do it. And while she might not be your biggest competitor, there are countless other content creators out there who are just waiting to steal your precious views. 

Valerie O'Connor

data annotation tools list

Master the art of video annotation with 5 uncommon tips and the top 7 tools

In today's digital era, video content is slowly but surely overpowering all other types of content, covering various industries and applications. Whether it's for feedback, research, machine learning, or creating engaging user experiences, video annotation plays a crucial role in extracting meaningful insights from video files. 

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Top 13 video annotation tools for feedback and training AI

The average person now spends about 17 hours a week watching online videos. That's a whole lot of time. And a whole lot of video content. So whether you’re creating online language lessons, trying to boost engagement for a growing brand, or working on a machine learning project, annotating videos faster is always a win! That’s where feedback and AI video annotation tools come in.

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Data Annotation in 2024: Why it matters & Top 8 Best Practices

data annotation tools list

Annotated data is an integral part of various machine learning, artificial intelligence (AI) and GenAI applications. It is also one of the most time-consuming and labor-intensive parts of AI/ML projects. Data annotation is one of the top limitations of AI implementation for organizations. Whether you work with an AI data service , or perform annotation in-house, you need to get this process right.

Tech leaders and developers need to focus on improving data annotation for their data-hungry digital solutions. To remedy that, we recommend an in-depth understanding of data annotation.

Our research covers the following:

What is data annotation?

  • Why it matters?
  • What its techniques/types are?
  • What are some key challenges of annotating data?
  • What are some best practices for data annotation?

Data annotation is the process of labeling data with relevant tags to make it easier for computers to understand and interpret. This data can be in the form of images, text, audio, or video, and data annotators need to label it as accurately as possible. Data annotation can be done manually by a human or automatically using advanced machine learning algorithms and tools. Learn more about automated data annotation.

For supervised machine learning, labeled datasets are crucial because ML models need to understand input patterns to process them and produce accurate results. Supervised ML models (see figure 1) train and learn from correctly annotated data and solve problems such as:

  • Classification: Assigning test data into specific categories. For instance, predicting whether a patient has a disease and assigning their health data to “disease” or “no disease” categories is a classification problem.
  • Regression: Establishing a relationship between dependent and independent variables. Estimating the relationship between the budget for advertising and the sales of a product is an example of a regression problem.

Figure 1: Supervised Learning Example 1

The image shows the supervised learning example. The training dataset has all kinds of fruits with different labels. the test set only has 2 types of fruit.

For example, training machine learning models of self-driving cars involve annotated video data. Individual objects in videos are annotated, which allows machines to predict the movements of objects.

Other terms to describe data annotation include data labeling, data tagging, data classification, or machine learning training data generation.

Why does data annotation matter?

Annotated data is the lifeblood of supervised learning models since the performance and accuracy of such models depend on the quality and quantity of annotated data. Machines can not see images and videos as we do. Data annotation makes the different data types machine-readable. Annotated data matters because:

  • Machine learning models have a wide variety of critical applications (e.g., healthcare) where erroneous AI/ML models can be dangerous
  • Finding high-quality annotated data is one of the primary challenges of building accurate machine-learning models

Here is a data-driven list of the top data annotation services on the market.

Gathering data is a prerequisite for annotation. To help you obtain the right datasets, here is some research:

  • Top data crowdsourcing platforms on the market
  • Guide to AI data collection.
  • Data-driven list of data collection/harvesting services.

What are the different types of data annotation?

Different data annotation techniques can be used depending on the machine learning application. Some of the most common types are:

Reinforcement learning with human feedback (RLHF) was identified in 2017. 2 It increased in popularity significantly in 2022 after the success of large language models (LLMS) like ChatGPT which leveraged the technology. These are the two main types of RLHF:

  • Humans generating suitable responses to train LLMs
  • Humans annotating (i.e. selecting) better responses among multiple LLM responses.

Human labor is expensive and AI companies are also leveraging reinforcement learning from AI feedback (RLAIF) to scale their annotations cost effectively in cases where AI models are confident about their feedback. 3

2. Text annotation

Text annotation trains machines to better understand the text. For example, chatbots can identify users’ requests with the keywords taught to the machine and offer solutions. If annotations are inaccurate, the machine is unlikely to provide a useful solution. Better text annotations provide a better customer experience. During the data annotation process, with text annotation, some specific keywords, sentences, etc., are assigned to data points. Comprehensive text annotations are crucial for accurate machine training. Some types of text annotation are:

2.1. Semantic annotation

Semantic annotation (see figure 2) is the process of tagging text documents. By tagging documents with relevant concepts, semantic annotation makes unstructured content easier to find. Computers can interpret and read the relationship between a specific part of metadata and a resource described by semantic annotation.

Figure 2: Semantic Annotation Example 4

The image shows an example of tagged words in a text document.

2.2. Intent annotation

For example, the sentence “I want to chat with David” indicates a request. Intent annotation analyzes the needs behind such texts and categorizes them, such as requests and approvals.

2.3. Sentiment annotation

Sentiment annotation (see Figure 3) tags the emotions within the text and helps machines recognize human emotions through words. Machine learning models are trained with sentiment annotation data to find the true emotions within the text. For example, by reading the comments left by customers about the products, ML models understand the attitude and emotion behind the text and then make the relevant labeling such as positive, negative, or neutral.

Figure 3: Sentiment Annotation Example 5

The image shows the process of labeling texts in documents

3. Text categorization

Text categorization assigns categories to the sentences in the document or the whole paragraph in accordance with the subject. Users can easily find the information they are looking for on the website.

4. Image annotation

Image annotation is the process of labeling images (see figure 4)  to train an AI or ML model. For example, a machine learning model gains a high level of comprehension like a human with tagged digital images and can interpret the images it sees. With data annotation, objects in any image are labeled. Depending on the use case, the number of labels on the image may increase. There are four fundamental types of image annotation:

4.1. Image classification

First, the machine trained with annotated images then determines what an image represents with the predefined annotated images.

4.2. Object recognition/detection

Object recognition/detection is a further version of image classification. It is the correct description of the numbers and exact positions of entities in the image. While a label is assigned to the entire image in image classification, object recognition labels entities separately. For example, with image classification, the image is labeled as day or night. Object recognition individually tags various entities in an image, such as a bicycle, tree, or table.

4.3. Segmentation

Segmentation is a more advanced form of image annotation. In order to analyze the image more easily, it divides the image into multiple segments, and these parts are called image objects. There are three types of image segmentation:

  • Semantic segmentation: Label similar objects in the image according to their properties, such as their size and location.
  • Instance segmentation: Each entity in the image can be labeled. It defines the properties of entities such as position and number.
  • Panoptic segmentation: Both semantic and instance segmentations are used by combining.

Figure 4: Image annotation example 6

An image showing the different types of image annotation including classification, Semantic segmentation, object detection, and instance segmentation.

5. Video annotation

Video annotation is the process of teaching computers to recognize objects from videos. Image and video annotation are types of data annotation methods that are performed to train computer vision (CV) systems , which is a subfield of artificial intelligence (AI).

Video annotation for a retail store surveillance system:

Click here to learn more about video annotation.

6. Audio annotation

Audio annotation is a type of data annotation that involves classifying components in audio data. Like all other types of annotation (such as image and text annotation), audio annotation requires manual labeling and specialized software. Solutions based on natural language processing (NLP) rely on audio annotation, and as their market grows (projected to grow 14 times between 2017 and 2025), the demand and importance of quality audio annotation will grow as well.

Audio annotation can be done through software that allows data annotators to label audio data with relevant words or phrases. For example, they may be asked to label a sound of a person coughing as “cough.”

Audio annotation can be: 

  • In-house, completed by that company’s employees.
  • Outsourced (i.e., done by a third-party company.)
  • Crowdsourced . Crowdsourced data annotation involves using a large network of data annotators to label data through an online platform.

Learn more about audio annotation.

7. Industry-specific data annotation

Each industry uses data annotation differently. Some industries use one type of annotation, and others use a combination to annotate their data. This section highlights some of the industry-specific types of data annotation.

  • Medical data annotation: Medical data annotation is used to annotate data such as medical images (MRI scans), EMRs, and clinical notes, etc. This type of data annotation helps develop computer vision-enabled systems for disease diagnosis and automated medical data analysis.
  • Retail data annotation: Retail data annotation is used to annotate retail data such as product images, customer data, and sentiment data . This type of annotation helps create and train accurate AI/ML models to determine the sentiment of customers, product recommendations , etc.
  • Finance data annotation: Finance data annotation is used to annotate data such as financial documents, transactional data, etc. This type of annotation helps develop AI/ML systems, such as fraud and compliance issues detection systems.
  • Automotive data annotation: This industry-specific annotation is used to annotate data from autonomous vehicles, such as data from cameras and lidar sensors. This annotation type helps develop models that can detect objects in the environment and other data points for autonomous vehicle systems.
  • Industrial data annotation: Industrial data annotation is used to annotate data from industrial applications, such as manufacturing images, maintenance data, safety data, quality control, etc. This type of data annotation helps create models that can detect anomalies in production processes and ensure worker safety.

What is the difference between data annotation and data labeling?

Data annotation and data labeling mean the same thing. You will come across articles that try to explain them in different ways and make up a difference. For example, some sources claim that data labeling is a subset of data annotation where data elements are assigned labels according to predefined rules or criteria. However, based on our discussions with vendors in this space and with data annotation users, we do not see major differences between these concepts.

What are the main challenges of data annotation?

  • Cost of annotating data: Data annotation can be done either manually or automatically. However, manually annotating data requires a lot of effort, and you also need to maintain the quality of the data.
  • Accuracy of annotation : Human errors can lead to poor data quality, and these have a direct impact on the prediction of AI/ML models. Gartner’s study highlights that poor data quality costs companies 15% of their revenue.

What are the best practices for data annotation?

  • Start with the correct data structure: Focus on creating data labels that are specific enough to be useful but still general enough to capture all possible variations in data sets.
  • Prepare detailed and easy-to-read instructions: Develop data annotation guidelines and best practices to ensure data consistency and accuracy across different data annotators.
  • Optimize the amount of annotation work: Annotation is costlier and cheaper alternatives need to be examined. You can work with a data collection service that offers pre-labeled datasets.
  • Collect data if necessary: If you don’t annotate enough data for machine learning models, their quality can suffer. You can work with data collection companies to collect more data.
  • Leverage outsourcing or crowdsourcing if data annotation requirements become too large and time-consuming for internal resources.
  • Support humans with machines: Use a combination of machine learning algorithms (data annotation software) with a human-in-the-loop approach to help humans focus on the hardest cases and increase the diversity of the training data set. Labeling data that the machine learning model can correctly process has limited value. 
  • Regularly test your data annotations for quality assurance purposes.
  • Have multiple data annotators review each other’s work for accuracy and consistency in labeling datasets.
  • Stay compliant: Carefully consider privacy and ethical issues when annotating sensitive data sets, such as images containing people or health records. Lack of compliance with local rules can damage your company’s reputation.

By following these data annotation best practices, you can ensure that your data sets are accurately labeled and accessible to data scientists and fuel your data-hungry projects.

You can also check our video annotation tools list to choose the fit that best suits your annotation needs.

If you have questions about data annotation, we would like to help:

External links

  • 1. Diego Calvo. (2019). Supervised learning. Diego Calvo. Accessed: 29/September/2023.
  • 2. Christiano P.; Leike J.; Brown T.B.; Martic M.; Legg S.; Amodei D. (2017). “ Deep reinforcement learning from human preferences “
  • 3. Bai Y.; et al. (2022). “ Constitutional AI: Harmlessness from AI Feedback ”. Retrieved January 1, 2024
  • 4. Articles Hubspot. (2019). What Is Text Annotation in Machine Learning, Examples and How it’s Done? . Accessed: 29/September/2023.
  • 5. Sentiment Annotation – Quick Start Guide. Accessed: 29/September/2023.
  • 6. Ashely John. (2020). Why Data & Data Annotation Make or Break AI. Medium. Accessed: 29/September/2023.

data annotation tools list

Next to Read

Quick guide to video annotation tools and types in 2024, video annotation: in-depth guide and use cases in 2024, top 10 open source data labeling/annotation platforms in 2024.

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Top 8 Annotation Tools (Features, Pros, Cons, Pricing)

Erica Chappell

Managing Editor

February 13, 2024

Looking for the best annotation software ?

Whether it’s business professionals or teachers, when asked to choose between scribbled notes and a well-labeled document , we’re sure they’ll pick the latter. 

Annotation Software

Annotation software marks a document or piece of content with anything a user wants, like text , an image , shapes, signatures, and more!

A color-coded and properly labeled document helps present your thoughts clearly.  

And annotation tools help you do just that.

But with hundreds of such software in the market, which one should you pick?

In this article, we’ll cover what an annotation tool is and highlight eight popular annotation software available today. We’ll go over their key features, pros, cons, and pricing to help you pick the best one.

What Is an Annotation Tool? 

2. filestage, 4. annotate, 5. pdf annotator, 6. drawboard projects, bonus tool: markup hero.

Let’s get started!

An annotation tool is a text or drawing tool that helps you add information to text, an image, a database , or any other piece of content.

But that’s not all.

These tools can also be used on various formats such as a whiteboard or PowerPoint presentation , to annotate the selected content. And we’re not just talking about business or education content.

Annotation software is even used in fields like gene ontology ! 

For example, Genome annotation or DNA annotation tools help annotate the gene location in a genome .

Not the DNA that you’re thinking of!

Click on the above gif to know what we meant.

And that folks, was an example of web annotation . 

What’s web annotation ?

A web annotation is the addition of information to resources present on web pages. 

For example, adding tags to GIFs of Korean boy bands in a blog post is an example of a web annotation . 

Now, while we’re on the topic of examples…

What are annotations examples?

Since annotation is essentially just marking and adding tags to sections of content, there are tons of examples out there.

Here are a few common ones:

  • Adding informative tags to an unlabeled dataset ( data labeling ) like a spreadsheet
  • Using a line tool or a pencil tool to draw on the screen during a presentation
  • Highlighting important text in a shared document with a highlighter tool
  • Labeling your lunch as “do not touch” when you store it in the company fridge

(Well, maybe not that last one)

But if you think annotation is just about adding manual notes to the content, think again. 

What are 3 types of annotations?

  • Manual annotation : the manual annotation type is exactly what it sounds like – it covers the process of manually annotating a piece of content
  • Screen annotation : the annotation of a desktop screen with the help of a screen marker software is known as screen annotation (you commonly use these during Zoom meetings and other video conferences)
  • Collaborative annotation : collaborative annotation is a teaching strategy to help students develop critical thinking, reading, and writing. 

Top 8 Annotation Tools in 2022

Here are the top eight annotation software that’ll make annotating your projects a cakewalk:

ClickUp is the world’s #1 project management tool .

But wait… you’re looking for annotation tools.

Why would a project management tool help?

That’s because ClickUp has advanced built-in annotation features to help you with all your annotation needs. And since it’s a comprehensive project management solution, you can use it for more than just annotation tasks !

From productivity monitoring to resource management , ClickUp can handle anything you throw at it!

Let’s see why ClickUp is the best annotation tool available today:

How do you annotate?

ClickUp’s Annotation feature supports PDF file and image annotation (.png, .gif, .jpeg, .webp).

You can easily add comments to task attachments in 4 simple steps:

  • Open the desired attachment within a task
  • Click “Add comments” in the upper right of the preview window
  • Click on the attachment preview wherever you want to add a comment
  • Add comments and assign them to anyone if you want them to take immediate action

Voila! 

Your added comment will now appear in the comments tab next to the attachment. Now the assigned comment will appear in the assignee’s Home tab as well as in the task’s comments section.

assigned comment for annotation

What’s more?

For added flexibility, you can even use ClickUp’s Chrome Extension for annotation and proofing documents. You can use the Chrome extension to markup images, upload them as a task attachment or add comments to the attachment — with ClickUp everything is possible!

Other key ClickUp features

  • Docs : easily create, share, and store project or company-related documents with your virtual or in-house team 
  • Comments : each ClickUp task offers a dedicated comment section that you can use to easily discuss and share any relevant file or links. You can even tag team members to ensure your comments aren’t overlooked.
  • Collaboration Detection : know when your teammates are simultaneously adding a comment or viewing a task in real-time. This can be extremely useful to overcome some common virtual team challenges you’d face in remote project management .
  • Notepad : a free space to jot down your thoughts and ideas quickly
  • Mind Maps : create free form mind maps to plan and organize your project-related ideas

ClickUp pros

  • User-friendly interface
  • Offers a feature-rich  free version
  • Can easily assign tasks to team members
  • Easy task management with the drag-and-drop feature 
  • Efficient resource management with more than one assignee for a specific task 
  • Manage and track your project timeline with intuitive Gantt Charts
  • Custom Access Rights help to share your projects with clients without compromising privacy
  • Can quickly find all your project resources with the Smart Search feature
  • Attempt project tasks in the right order with Task Dependencies
  • Agile dashboards to measure your project progress with charts like Velocity , Burnup , Burndown , and Cumulative Flow  
  • Can customize how you receive project notifications
  • Offers app integrations with various third-party apps for seamless project management
  • Available as web browser , desktop, and mobile apps

ClickUp cons

  • No white-labeling available (can’t remove brand icon )
  • Can’t export Dashboards

But don’t worry, at ClickUp, we’re constantly working to overcome such minor flaws.

Click here to learn more about what’s cooking in the ClickUp kitchen.

ClickUp pricing

ClickUp has three pricing options:

  • Free Forever Plan : includes unlimited projects and members + 100MB of cloud storage space 
  • Unlimited Plan ($7/user per month) : supports unlimited projects and members + forms + unlimited number of integration + advanced reporting + unlimited Gantt charts
  • Business Plan ($12/user per month) : includes unlimited projects and members + unlimited Proofing + timesheets + two-factor authentication + sprint points + Google single sign-on + custom exporting

filestage

Filestage is a good proofing and annotation tool to help you annotate a PDF file as well as videos and audios. 

Filestage key features

  • Add annotation to comments with markup tools
  • Add attachments to include reference files in comments
  • Use their dashboards to manage all review-related workflows
  • Threaded comments facilitate clear discussions
  • Easily review audio and video files with time codes

Filestage pros

  • Can easily track file status during the review process
  • Can add due dates to avoid missing deadlines
  • Easily integrates with third-party apps
  • Can add attachments in comments to enhance collaboration

Filestage cons

  • Cannot undo an approved annotation action
  • No support for web annotation
  • Cannot assign tasks to team members

Filestage pricing

Filestage offers three pricing tiers:

  • Starter ($99/month) : includes 15 active projects + basic project structure + basic support 
  • Pro ($249/month) : includes 35 active projects + advanced project structure + priority support + 10 team members + project template
  • Enterprise (custom pricing) : supports more projects + custom workflows + more team members

prodigy

Prodigy is a data annotation tool that creates evaluation and training data for machine learning models. And while this data tool could act as a prodigy for annotation, it’s not what’d you call affordable for most teams.

Prodigy key features

  • Offers text annotation for any script or language
  • Allows text classification with multiple categories
  • Annotate NLP (Natural Language Processing) relations to label your data
  • Image annotation tool for tasks like object detection, image classification, and segmentation 

Prodigy pros

  • Suitable for novice users
  • Along with text and image annotation , can annotate audio clips as well
  • Evaluate any model with randomized A/B testing
  • Offers live demo and documentation for ease of use

Prodigy co ns 

  • Supports collaboration annotation only for small teams
  • You’ll need a separate tool for managing projects
  • Doesn’t offer advanced semantic segmentation feature

Prodigy pricing

Prodigy comes with two pricing plans:

  • Personal ($390/lifetime license) : offers a lifetime license + unlimited projects + unlimited annotator  
  • Company ($490/seat) : includes “personal” features + flexible and transferable floating licenses

annotate

Annotate is a document collaboration platform that helps make the document annotation process a breeze, from the first draft to the final sign-off. However, if you’re looking to annotate on the move, this might not be the tool for you.

Annotate key features

  • Perform text or image annotation with the help of figures like text box , polygon or freehand notes
  • Easily create notes within a document and assign them to team members 
  • Communicate over document and workspace chat while reviewing documents
  • Draft your own documents and export as Word or PDF documents

Annotate pros

  • Supports custom integrations with third-party apps
  • Ability to create workflows within documents
  • Restricted access for external collaborators

Annotate cons

  • Doesn’t offer Android and iOS mobile apps
  • No support for task and project management
  • No calendar feature to manage your work schedule

Annotate pricing

Annotate offers three pricing plans:

  • Free : supports up to 3 users + text annotation + document workspaces + full text search + in document collaboration 
  • Standard (contact the sales team) : minimum 3 users + includes “Free” features + freehand annotation + notifications + document metadata and tagging
  • Enterprise (contact the sales team) : includes “Standard” features + unlimited storage + unlimited users + document comparisons

pdf annotator

PDF Annotator is a PDF annotation tool that lets you add notes and annotations to your PDF documents . This annotation software enables you to add comments, signatures, and designs,  making PDF annotation super easy.

PDF Annotator key features

  • Can type your comments right into the document
  • Freehand annotations with the Pen Tool
  • Add stamps, signatures and page numbers to the document
  • You can easily add markup and insert images 
  • Import and export pdf annotation across different document versions

PDF Annotator pros

  • Offers an embedded image editor
  • Convert your document to any format like doc, xls or ppt
  • Can extract, move or delete selected pages from a document

PDF Annotator cons

  • Adobe DRM protected documents cannot be opened
  • Only available for Windows operating systems
  • Cannot annotate digital signature protected documents 

PDF Annotator pricing

PDF Annotator offers 3 buying options (prices vary for 4 users and above): 

  • Annotator License: $69.95/ user
  • Student License : $39.95/ user
  • Faculty License: $69.95/ user

drawboard tool

Drawboard Projects is a PDF markup software tool that offers a wide range of markup features like text tool and polygon tool . You can even annotate your documents with digital ink. However, if you’re looking to organize your annotations easily, you might want to look elsewhere.

Drawboard Projects key features

  • Markup any PDF file or drawing
  • Pin an issue to a specific area on drawings to provide context
  • Offers a time-restricted review link for guest access
  • Add tags to a file to enhance the searchability 

Drawboard Projects pros

  • Collaborative markup and text annotation
  • Can easily assign and track issue progress
  • Integration with apps like Google drive and Procore
  • Offers offline documentation support

Drawboard Projects cons

  • Cannot organize projects into folders
  • Only offers Windows 10 and iOS apps
  • Integration with limited applications

Drawboard Projects pricing

  • Basic ($24/ user per month) : supports up to 1000 drawings or documents + unlimited projects + third party app integrations 
  • Standard ($44/ user per month) : offers unlimited drawings or documents + unlimited projects + email support + third party app integrations
  • Enterprise (contact Drawboard team) : includes “Standard” features + federated access logins (SSO) + priority support

doccano

Doccano is an open-source text annotation tool . With its simple user interface, you can easily upload your desired dataset and start annotating.

Doccano key features

  • Ability to perform sentiment analysis on text data
  • Offers text classification that can be exported as plain text, CSV, JSON formats
  • Supports data annotation feature of sequence labeling
  • Sequence to sequence learning for training data models

Doccano pros

  • Simple, user-friendly  interface
  • Auto label with trained machine learning models
  • Can annotate text of any language
  • Open source annotation software

Doccano cons

  • Can lag and become unresponsive at times
  • Can shuffle the annotated pieces only when self-hosted
  • Requires coding-heavy setup

Doccano pricing

As Doccano is open source , it’s a free annotation tool .

ink2go

Ink2Go is a simple screen recording and screen annotation software . Its user-friendliness makes it an excellent candidate for any live presentation or seminars.

Ink2Go key features

  • Write, draw or highlight any content on desktop or apps like Sticky Note
  • Screenshot , screen capture or record your desktop with the added annotations
  • Offers a built-in digital whiteboard or blackboard for better user experience
  • Annotate across multiple pages simultaneously

Ink2Go pros

  • Supports webcam for presentation and seminars
  • Can annotate on extended or shared screen
  • Floating drawing tool bar with the ability to align both horizontally and vertically
  • Multiple pens, highlighters, and shapes like a rectangle for high-quality presentations

Ink2Go cons

  • Limited toolbar items
  • Irregular software version releases
  • No live customer support

Ink2Go pricing

Ink2Go offers both Windows and Mac apps with a 15-day free trial. However, a single license costs $19.99.

data annotation tools list

Markup Hero is a screenshot and annotation tool that is web based, easy to use, and includes a bunch of helpful markup controls including: callouts, highlight, signature, crop, pen, shapes, colors, blur, and more. In Markup Hero, annotations are not flattened so you can edit you markup forever. Organize annotations with collections, search and naming, and share instantly with a link or copy to clipboard.

Markup Hero key features

  • Always editable markups
  • Multi-page markups and insert images
  • Share links with permissions
  • Chrome Extension for scrolling screenshots

Markup Hero pros

  • Annotated images, PDF’s, Google Docs, and Websites
  • Works on any device including mobile
  • Good for screenshots and annotations
  • Easily embeddable into Slack, social and even ClickUp tickets

Markup Hero cons

  • No video capture
  • No team accounts (yet)
  • No custom domains

Markup Hero pricing

Markup Hero has a forever free plan with limited monthly annotations and you try the tool without even making an account. Unlimited markups and full features on the Super Hero plan is only $4.00 per month.

A well placed, detailed annotation is one of the easiest ways to add value to your content. It’s what can transform a boring piece of data into something as exciting as this:

Well, maybe not that exciting.

But you get the point.

Luckily, choosing an annotation software that fits your needs doesn’t have to be as difficult as perfecting English grammar!

Just go through the 8 options for annotation tools we covered here for a head start. 

And while each of these tools is great, ClickUp smashes it out of the park with its powerful features that help you proof your documents easily. 

From image and PDF proofing to a feature-rich  Forever Free plan, this versatile annotation software hits the home run.

Sign up for ClickUp today to present your perfectly annotated ideas to the world! 

Questions? Comments? Visit our Help Center for support.

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data annotation tools list

Data Annotation Tools: 5 Best Tools for Annotating Data in 2023

Explore 2023's top data annotation tools for AI success. Dive into features, benefits, and best fit use cases of the five best tools for data annotation.

data annotation tools list

IN THIS ARTICLE

5 minute read

The rapid growth of AI and machine learning has brought about a surge in the need for high-quality, annotated data, since data is the backbone of training models to perform tasks with precision. The process of data annotation can be time-consuming and requires specialized data annotation tools to ensure accuracy and efficiency.

data annotation tools list

But w hat is data annotation?

Data annotation is the process of labeling raw data, like images, text, or videos, in order to make it understandable for machine learning models.

In other words, think of it as giving context to data. For instance, in an image of a cat and a dog, data annotation would involve labeling or marking the cat and the dog separately, so the machine learning model can recognize and differentiate between the two in future tasks.

However, one of the most important parts of annotation is building out a set of labels. Read about our best practices for creating annotation labels here .

What are data annotation tools?

Data annotation tools are software solutions designed to facilitate the process of labeling and annotating data.

That is to say, these tools offer features like image segmentation, bounding boxes, and text annotation, all the tools that make it easier for data scientists and annotators to prepare data for machine learning models.

Moreover, most top annotation tools today are no-code , making them more accessible. As a result, it’s never been easier to gather high quality annotated data for your projects — as long as you’re using the right tools.

That’s why, we went through a variety of sources online, and read through hundreds of reviews to find the five best data annotation tools for 2023. Let’s find out what they are:

Top 5 Data Annotation Tools for 2023

Cvat: best for image & video annotations.

CVAT is a an Open Data Annotation Platform that’s excellent for vision tasks like image classification, object detection, 3D cuboids and more.

This data annotation tool was developed by Intel in 2017, and is now under OpenCV, therefore putting it under the wing of one of the “OGs” of AI and data processing.

Key Features:

  • Frame-by-Frame Annotation: Allows detailed annotations for every frame in videos.
  • Varied Annotation Types: From polygonal segmentation to bounding boxes, CVAT offers diverse options.
  • Cloud Features: Share your project with your colleagues and team members on the cloud.

Advantages:

  • Use AI-enabled annotation using an integrated AI or even by uploading your own model using your own data, therefore making the annotation process faster.
  • You can store your datasets privately in the cloud on AWS S3, Google Cloud Storage or Azure Blob Storage, leading to more flexibility.
  • Strong community of 60,000+ developers, enabling you to get support even when using the community-supported version.

Disadvantages:

  • No support for text annotation.

Why choose CVAT as your data annotation tool?

CVAT stands out for its capabilities in both image and video annotations. Its detailed features ensure precision, therefore making it a top choice for multimedia projects.

It’s available on-premise and on-cloud, and has free plans available.

Kili: Best for Integration

Kili is a powerful data annotation tool that makes it easy to integrate with cloud storage, model training tools, and external systems easily using its API and user interface.

  • Collaborative Environment: Multiple annotators can work simultaneously, speeding up the annotation process.
  • Flexibility: Kili supports a variety of data types, from text to images.
  • Some people reviewed that the tool hasn’t given any problems even when a workforce of 1000+ people were using it at the same time on one project, thus making the tool very stable.
  • It has SSO features, therefore making it easy to integrate Kili into your workforce management tools if you’re a large organisation.
  • It has an accommodating and reactive customer support team,
  • There is no possibility of text annotation within images without uploading OCR data, which can limit your workflow.
  • Users are unable to upload Excel or Word data without using the API, consequently making it more complex if you work with data in those formats.
  • It’s an online platform, so there’s no personal interaction and help from the team on deeper aspects of annotation.

Why choose Kili as your data annotation tool?

Kili's strength lies in its flexibility and collaborative features, therefore making it suitable for diverse annotation needs.

Kili has a limited free plan available, allowing you to get started.

Labelstudio: Best Open-Source Tool

Labelstudio is a completely open-source data annotation tool that can annotate text, images, audio, video, and also time series and mixed data.

  • Customizable Workflows: Tailor the tool according to your project's needs.
  • Extensive Community Support: Being open-source, it has a vast community contributing to its growth and offering support.
  • Open-source with strong community support. Open-source tools are generally safer and more secure since their code-base is open for everyone to see.
  • Lots of different data types supported — text, audio, image, video and time series.
  • Hotkey feature that makes the interface more seamless labeling, increasing the number of labels your agents can add.
  • Machine learning automation to increase efficiency.
  • The tool is possibly too technical for most people, therefore making it slightly inaccessible to some.

Why choose Labelstudio as your data annotation tool?

For those who prefer open-source tools and want the freedom to customize, Labelstudio is the go-to choice.

Labelstudio has a free community edition available, along with a cloud or on-premise enterprise version, enabling you to choose the right one for your needs and stage.

Labelbox: Customizable Annotation

With Labelbox , you can customize tasks to fit your unique needs with features that give you full control in your data annotation workflow.

  • Tailored Annotation Environment: Customize the tool to fit your specific annotation requirements.
  • Integration Capabilities: Easily integrate with machine learning platforms and other tools.
  • Automation: Labelbox has features like data split management, auto-segmentation and model-assisted pre-labeling.
  • Extremely easy to use UI, with tools to make tasks much clearer, and easy to use shortcuts to make you faster.
  • Annotation analytics are very easy to access — admins can easily check how long agents take to complete labels, and track metrics like total labels per agent or team.
  • Very proactive team, fixes issues as they come up.
  • Data migration can be hard to perform.
  • Labelbox can’t handle multichannel images that are commonly used in scientific imaging.

Why choose Labelbox as your data annotation tool?

Labelbox stands out for its high level of customization, allowing businesses to tailor the tool to their unique needs.

It has a limited free version available, along with pay as you go plans enabling you to control costs as you scale.

Lettria: The All-In-One NLP Tool

Finally, we have Lettria , the all-in-one NLP tool. Lettria contains data annotation tools focused on text and audio (after transcription).

The best part with Lettria is that it’s an all-in-one tool: this means that you can not only annotate your data, but you can also manage your datasets, build your taxonomy, train your model, and deploy it to production — all in one single platform without any lines of code.

  • One-Shot and Zero-Shot Classification: Lettria’s NLP algorithms make annotation a lot easier by providing in-line suggestions.
  • NLP Specialization: Designed specifically for natural language processing tasks.
  • Model Training: Once your data is annotated, you can easily train a machine learning model using the data and annotations, using the same tool.
  • All-in-one platform lets you build your entire AI text processing pipeline in one place, without switching tools, therefore enabling you to create your entire pipeline faster.
  • Lettria is collaborative and allows for no-code text annotation, so as to gather inputs from everyone in your team.
  • The team is responsive and customer support is available to help you get started with text processing, regardless of your company size.
  • Lettria isn’t a dedicated annotation platform, so some features might be missing from more dedicated platforms.

Why choose Lettria as your data annotation tool?

For businesses focusing on text data and NLP, Lettria offers a comprehensive solution that caters to all NLP annotation needs.

Moreover, if you don’t want to juggle different tools for data management, annotation, training and deployment, Lettria is a great choice since it’s an all-in-one platform.

Lettria has a limited free trial available to try it out for yourself, and you can reach out to us to get started when you’re ready!

Choosing among the right data annotation tools is important for the success of your AI and machine learning projects. The tools listed above are the best in the industry for 2023, catering to a range of needs and budgets.

Investing in effective data annotation tools ensures that your machine learning models are trained with high-quality, accurately labeled data. This, in turn sets the foundation for successful AI projects within your company.

Remember, in the world of AI, the quality of your data often determines the quality of your outcomes. Choose wisely!

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Annotation tools for building datasets

A list of the best open-source annotation tools for labeling data., use this form to add new tools to the list..

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Blog » Online Proofing » 13 Best Annotation Tools to Try

13 Best Annotation Tools to Try

  • Sara Dimovska
  • August 23, 2023

data annotation tools list

Are you looking to markup files and documents? Wondering what are the best annotation tools to use?

Table of Contents

FACT: Collaborating on content gets so messy sometimes. Great ideas get lost in endless email chains. Feedback and comments often lack context, making them impossible to act on. And even when you’re trying to explain them better, there’s still something missing. 

But it all changes when you can add comments directly on the piece of content. When you can annotate an image or video, leave comments beside a paragraph of text, mark elements on web pages that need changing, etc.

Enter annotation tools, software that allows content creators to do just that. 

In this guide, you’ll discover 13 of the best annotation tools today and learn how they can improve your feedback and approval workflows . 

So, let’s get to it. 

What Exactly is Annotation Software?

The term – annotation software – refers to tools that allow you to mark and draw on other files – images, videos, text files, PDFs, and more – to provide precise comments and instructions. 

Different annotation tools use different types of annotation, from simple text comments and notes to allowing users to color-code and label elements of a document, add signatures, and more. 

data annotation tools list

When to use the annotation software?

Organizations use annotation tools in a variety of situations. Here are just some examples:

  • Many companies implement annotation software to comment and provide contextual feedback on content assets across the entire content production process. Such platforms help team members provide more meaningful feedback that can be acted on quickly. 
  • Annotation tools help teachers highlight important passages in text files submitted by students. 
  • Many organizations use annotations to add suggestions for improvements to various assets they work on and data and other file formats. 
  • Teachers and educational institutions often use collaborative annotation to help students develop their reading, writing, and critical thinking skills.

Overall, annotation tools are ideal when you need to provide meaningful feedback and leave comments directly on a file or a data set. 

But there’s a problem, right? Search for annotation tools in Google, and you’re immediately overwhelmed by the number of options available. 

How do you decide which software to use?

Here are some suggestions for how to evaluate the tool. And further down in this guide, you can review 13 of the best annotation tools today to evaluate for your annotation process.

What to Consider When Selecting an Annotation Tool

Most annotation tools focus on specific uses or use cases. Some help with annotating files. For others, annotation features are only part of a larger project management platform that helps companies deliver content more efficiently. 

For that reason alone, you should evaluate each tool against these three criteria:

#1. Functionality

Consider what you will be using the tool for. Do you need it just to annotate files? Or does your team struggle with creating content efficiently? Some annotation tools will help you improve feedback; others will also provide features to scale content production. 

Each of the tools on our list has its distinct set of features. So, as the first thing, check whether the software’s key features match what you’ll be using it for. 

#2. Annotation capabilities

Secondly, different tools focus on different file formats. Most allow marking up text or PDFs. Many offer image annotation options, and some also allow you to annotate videos or databases. 

When evaluating annotation tools, also pay attention to what file types the tool works with. 

#3. Efficiency

Annotating files seems simple to do. But from the software point of view, it’s a complex process that requires adding more information to an existing file. Some tools handle this better than others. They work slower on certain file types and may even struggle to process more complex assets. 

So, if you find tools you like, take them for a trial. See how well the tool performs tasks and if you’re happy with the speed and efficiency.

With all that in mind, here are the best annotation tools to consider.

13 Best Annotation Tools for 2023

data annotation tools list

CELUM ( disclaimer: this is our tool ) is one of the most advanced online proofing tools, with equally advanced annotation features. 

Because of that, CELUM offers an incredible range of annotation capabilities:

  • The ability to share precise and actionable feedback directly on files
  • Annotations on videos, images, text, PDFs, and other common file formats. 
  • CELUM gives you access to a complete revisions history to compare different versions of files and monitor how projects evolve. 
  • Mentions to notify other team members about new annotations.
  • Email notifications and triggers to keep everyone informed, and more. 

But CELUM offers a whole range of other capabilities to help your team create content faster and at a scale. 

  • Online proofing that makes review and approval processes a breeze.
  • Content hub to keep all assets in one place, easy for everyone to find.
  • Project management with advanced Kanban boards, tasks, and project templates. 
  • Content workflows will help you streamline creative processes.
  • Finally, a whole range of integrations will make CELUM an amazing part of your creative stack.

Pricing: CELUM offers a free plan for small teams of up to 10 people. Bigger teams can avail of paid plans with additional functionality starting at €14.90/per month. 

Looking for an annotation tool?

Want to see how CELUM can help your brand improve annotations and scale content production?

#2. Filestage

data annotation tools list

Filestage is another online proofing tool that offers annotation tools as part of its toolstack. With Filestage, teams can add feedback on almost all common file formats and add annotations in context. 

However, Filestage’s main focus is on centralizing review and approval processes and allowing teams to save time and just get on with their creative work. 

Pricing: Filestage pricing starts at €12 per user per month. Team plans start at €24 per month per seat.

#3. Annotate

data annotation tools list

Annotate is a document collaboration tool that helps teams take their documents from the first draft to a completed asset. Annotate focuses on allowing teams to create documents on time, and for that reason, the tool focuses on selected file formats only – text files, PDFs, etc. 

One of the unique features of Annotate is the ability to store documents on a private cloud or even on your servers. 

Pricing: Annotate offers a limited free plan. The company doesn’t disclose paid plans on the site, and you need to contact them directly for those. 

#4. Doccano

data annotation tools list

Doccano is open-source software that incorporates machine learning to expand traditional annotation features. With Doccano, you can perform sentiment analysis of your text, text classification, use data annotation tools, and even use sequence to sequence learning to create training data models. 

Pricing: Doccano is a free tool.

data annotation tools list

Unlike other apps on our list, Ink2Go is desktop software. This means that you need to install it on your computer. But it also means that you can use it on top of almost any other software you’re using, even on video. Ink2Go then lets you save those annotations as images to share further. 

Pricing: Ink2Go requires a one-time license of $19.90.

#6. PDF Annotator

data annotation tools list

As the name suggests, this annotation tool works with PDFs only. But it offers a whole range of features to annotate this file format. You can comment on any PDF, edit it, add corrections and highlights, and even add photos and drawings. You can then save your annotations in the original file to share them with your team and restore the original document without comments easily. 

Pricing: PDF Annotator is also a desktop tool, and a single license costs €69.95. 

#7. Markup Hero

data annotation tools list

MarkupHero is a screenshot and annotation tool that aims to speed up your content creation workflow. With MarkupHero, you can communicate with the team better through the screenshot app and image and PDF uploader. You can also annotate an image, PDF, or website using various annotation features. 

Pricing: MarkupHero offers a free plan. It also offers a paid plan at $4/mo per user.

#8. Zoho Annotator

data annotation tools list

Zoho Annotator is a screen capture and annotation browser plugin that helps to add contextual comments on web pages without leaving your browser. It’s an ideal tool for teams that regularly review website designs and need to provide comments and instructions to other stakeholders. 

With Zoho Annotator, you start by taking a snapshot of a website. Then, you use annotation tools to provide feedback, highlight potential errors or mistakes, etc. You can also blur or smudge confidential data before sharing the image with your comments.

Pricing: Zoho Annotator is a free Chrome extension.

#9. ClickUp

data annotation tools list

ClickUp is a robust project management platform bringing together various capabilities and tools to allow teams to do all of their work in one place. With ClickUp, you can plan projects, collaborate on documents, chat and exchange ideas, and more. 

But ClickUp also features an annotation tool that allows you to markup PDFs and images directly from the tool. 

Pricing: ClickUp offers a free plan. Paid plans start at $9 per month per user.

#10. GoVisually

data annotation tools list

GoVisually is an approval and review software helping companies move away from offering feedback on content via email. Instead, the company offers a whole range of proofing solutions to provide visual feedback and annotations directly on design files, images, PDFs and video content.

With GoVisually, you can upload your files to the platform, add feedback and mark them up before sharing them further with other stakeholders. 

Pricing: GoVisually offers two pricing plans. The first one costs $25 per month and is ideal for freelancers. The Team plan starts at $60 a month for 5 users. 

data annotation tools list

Diigo is another Chrome extension that allows you to annotate and markup online PDFs and webpages directly from the browser. With Diigo, you can add sticky notes, highlight content sections, place tags, outline documents, and more. 

On top of that, Diigo lets you build a list of bookmarks, tag your collected web pages, and share your research with colleagues. 

Pricing: Diigo offers a limited free plan. Paid plans start at $40 per year. 

#12. Superannotate

data annotation tools list

Superannotate is quite a unique annotation tool on our list. It does provide a whole range of capabilities to annotate data. However, its focus is on annotating and managing ground truth for artificial intelligence. 

With SuperAnnotate, you can create high-quality training datasets for object detection, instance, semantic segmentation, keypoint annotation, data labeling, or video tracking. 

Pricing: SuperAnnotate does not make its pricing public.

#13. RedPen

data annotation tools list

RedPen is all about making content collaboration as easy and fast as possible. The tool allows you to upload designs, add comments and annotations, and ask colleagues for feedback. You can also organize your work into projects to ensure that all assets are in one place and keep track of each file revision. 

Pricing: RedPen offers pricing plans based on the number of projects. Plans start at $20/mo for 5 projects.

And there you have it…

Thirteen of the best annotation tools to capture feedback in context, and speed up team collaboration.

All that’s left for you is to review each of them and pick the one that suits your needs the most. 

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Data Annotation and What Data Annotation Companies do

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  • Infographic – A Comprehensive List of Data Annotation Tools

We summarized a list of data annotation tools in an infographic which helps you to understand:

  • How many types of data annotation tools there are
  • The best tools in each type
  • What kind of data that the tools can label

[Infographic] A Comprehensive List of Data Annotation Tools

Infographic - Types of Data Annotation Tools

Types of data annotation tools

Open source tools.

The open source tools are free to use and it allows people to modify the source code. Some of the open source tools for data annotation are: CVAT , LabelImg , Doccano , VoTT.

Commercial tools

Using this type of tool will require payment, however, it allows users to customize the functions to suit their need. The best commercial tools can be named:  LinkedAI , Dataloop AI , Tagtog , LightTag.

Freeware tools

Similar to open source tools, freeware tools do not charge any fees. You can download and use freeware but cannot modify them. An example of this type of tool is Colabeler , SuperAnnotate

You can walk through our video guidance series about AI Data Labeling Tools if you still wonder how to choose the best tools for your data labeling project.

Want to improve the efficiency of the data labeling process? Book a meeting with us!

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Title: large language models for data annotation: a survey.

Abstract: Data annotation is the labeling or tagging of raw data with relevant information, essential for improving the efficacy of machine learning models. The process, however, is labor-intensive and expensive. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to revolutionize and automate the intricate process of data annotation. While existing surveys have extensively covered LLM architecture, training, and general applications, this paper uniquely focuses on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Data Annotation, Assessing LLM-generated Annotations, and Learning with LLM-generated annotations. Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation. As a key guide, this survey aims to direct researchers and practitioners in exploring the potential of the latest LLMs for data annotation, fostering future advancements in this critical domain. We provide a comprehensive papers list at \url{ this https URL }.

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May 3, 2022

Ultimate list of free and paid best image annotation tools

What are annotation tools and where are they used? 

Judging by the current rate of data creation, data annotation is no easy task, and the ability of the algorithms we use today to perform effectively depends on data annotation. Computers can't interprete situations or provide context the way humans can so data annotation was birthed to make those connections. It is the human-in-the-loop approach of labeling content like text and audiovisuals (images and video) so they can be correctly recognized by ML models and used to make accurate predictions. 

What is image annotation 

Image annotation in machine learning and deep learning can be defined as the process of annotating an image with labels, usually involving a human-in-the-loop approach and the occasional computer assisted help. It involves classifying an image using annotation tools, to show the data features you want your model to recognize on its own and it is mainly done so the system will be able to recognize objects with greater precision. This is usually done with datasets that are used to train ML algorithms. 

Difference between image annotation and labelling 

Image labeling and image annotation are phrases used interchangeably when trying to describe the art of classifying or identifying images for training machine learning algorithms. Both processes need to be executed with great precision and clarity with their major difference being that annotation helps the system recognize relevant data through computer vision whereas labeling is used for training advanced algorithms to recognize patterns in future so it'll be able to make decisions on its own.

3 free image annotation tools

1. Isahit lab: This is an image annotation tools that is extremely easy to use which allows you to annotate an unlimited number of images for free. It also allows you to invite teams and users to assist you on your annotation projects. Isahit spent years developing this intuitive annotation tool and were able to draw from their experience in handling labeling projects through their outsourcing platform.

2. CVAT: CVAT is an acronym which stands for Computer Vision Annotation Tool and it is a free open source annotation tool which is developed by Intel and it also supports video annotation. It also has a user-friendly interface which makes annotating easier. To start using CVAT,  you'll need to create an account first on cvat.org after which you'll be given access to the tool and you can start annotating your data.

3. Labelme: Labelme is an open source annotation tool that you could use to process image datasets. A key feature to be noted on the tool is the “File List” option on the bottom right. This could come in handy when you have a lot of images to annotate, because it reduces the chances of you missing any images. The only disadvantage of labelme is that your file can only be saved in JSON format. 

3 paid image annotation tools

1. V7: V7 is an automated annotation tool that combines dataset management, image and video annotation, and auto machine learning model training to complete annotation tasks. The platform enables teams to store, manage, annotate, and automate their data annotation workflows in videos, images, medical data and other formats. Price options start from $150.

2. Labelbox: Labelbox offers AI-powered labeling tools, labeling automation, human workforce, data management, among other services along with a powerful API for integration. The platform offers a superpixel coloring option for semantic segmentation and a friendly user interface.

3. Data loop: This is an all-in-one cloud-based annotation platform with embedded tools and automation capable of producing high-quality datasets. The platform makes provision for the entire AI lifecycle including annotation, model evaluation, and model improvement by using a human in the loop approach. It also offers tools for basic recognition tasks like detection, classification, key points, and segmentation while also supporting both image and video data. It also has advanced team workflows with streamlined data indexing and video support.

Key criteria for reviewing image annotation tools

When doing image annotation, it is very important that you choose a very good tool as it will directly affect the quality of your processed work. There are certain criteria you should take into consideration such as functionality, efficiency, formatting, application and price to be able to find a tool that adequately fits your needs. There are many image annotation tools out there and you have to do your research carefully to make sure you choose a tool that works best for you and your project. 

Outsourcing image labeling - The solution for large-scale annotation projects

If you have a large volume of images that need to be labeled, outsourcing can be a smart solution. Companies like isahit offer image labeling services that not only provide you with the annotation tool, but also a trained and qualified workforce to annotate your data accurately and efficiently.

With isahit, you can trust that your annotation project is in good hands. Their diverse and qualified workforce is committed to providing you with high-quality labeled data, ensuring that your project is completed on time and to your satisfaction. By outsourcing your image labeling needs, you can save time and resources, and focus on other important aspects of your project.

Each unique project has a specific need when it comes to annotation tools. One might need either an image annotation tool, a text annotation tool, a video annotation tool or some combination of the above. With so many tools to choose from, finding the right fit can be a frustrating process. Not to worry though- we’ve rounded up a master list of the best open source annotation tools in 2022. Keep reading to find the best annotation tool for your unique needs!

Best Image and Video Annotators

The Computer Vision Annotation Tool is a powerful and efficient image and video annotator. It is open source and web based, and though its user interface is not very intuitive, amateurs and professionals will be able to take advantage of it after getting over the learning curvet.

Key Features:

  • Maintained by Intel
  • Annotation is semi-automatic
  • Third Party Integrations
  • Easy to deploy on local networks
  • Tutorials available online
  • Supports a variety of file types
  • No customer support
  • CVAT has to be maintained as it scales
  • No pdf file support
  • Learning curve; the UI may take several days to master

Source code- https://github.com/opencv/cvat

Labelimg has been around for over 5 years, and is one of the popular, dependable tools for graphic image labelling on the web. It has a simple interface which is also pretty intuitive, making it pretty easy to work with.

Key Features

  • Written in Python
  • Graphical Interface is in QT
  • Annotations are saved and exported as XML files
  • Needs to be installed locally
  • Can be used offline for added security
  • Simple, intuitive interface, even for beginners
  • Default version offers only one annotation type (bounding box)

Does not offer video annotation

Source code- https://github.com/tzutalin/labelImg

LabelMe is an open source dataset of digital images with annotations. Free to use, it was created by the MIT Computer Science and Artificial Intelligence Laboratory in 2008, and users are allowed to contribute to the library. It has a voluminous library, described by some as canonical.

  • Made by MIT Computer Science and Artificial Intelligence Laboratory
  • Six different types of annotations offered
  • Customizable UI
  • Can be used both online and offline
  • Files can only be exported and saved in JSON format
  • No program management capabilities
  • Low level of precision

OpenLabeling  

OpenLabeling is a sturdy tool for both image and video annotation in computer vision applications Created by João Cartucho, this tool was licensed in 2018.

  • Runs in Python
  • Features a pre-trained model 
  • Multiple annotation formats are available, for example PascalVOC and YOLODarknet
  • Deep Learning feature available
  • Both Python and OpenCV have to be downloaded to use this tool

Source code- https://github.com/Cartucho/OpenLabeling

Best Text Annotation Tools

Developed to annotate chunks of text, YEDDA is able to work in many languages including English and Chinese. Text, symbols and even emojis can be accurately annotated by this super tool.

Yedda also supports shortcut annotation which increases efficiency in annotating text by hand.

  • Runs in Python, requires Python preinstallation
  • Supports shortcut annotation
  • Collaborative capability
  • User Interfaces both for administrators and annotators
  • Users complain of a few bugs while using this tool
  • Not available offline

Source code https://github.com/jiesutd/YEDDA

ML-Annotate

Another popular open source text annotator, ML-Annotate is one of the first choices for many when it comes to text annotation. Developed by 

  • Runs on Python
  • Administrative users can be added
  • UI is completely customizable, with instructions for making modifications provided
  • Supports multi-class,multi-label and binary labelling
  • Can be used offline
  • Data library not included.

Source code https://github.com/falcony-io/ml-annotate

We hope this was helpful! If you’re still undecided, you can check out our table below for the summarised version of all the info above.

Image and video annotation comparative table

data annotation tools list

Your AI project's success or failure will be determined by the data annotation tools you employ to enrich your data for training and deploying machine learning models. 

What is data annotation?

The process of labeling data to show the results you want your machine learning model to forecast is called data annotation in machine learning. You're marking up a dataset with the qualities you want your machine learning system to learn to recognize by labeling, tagging, transcribing, or processing it. 

What is a data annotation tool?

A data annotation tool is a solution that can be used to annotate industrial-grade training data for machine learning. It can be cloud-based, on-premise, or containerized. The following are essential features of data annotation software: Text, image, video, audio, time-series, and sensor data are all examples of data types that can be annotated with them. They support 2-D, 3-D, video, audio, transcription, and text annotation.

Important features of a data annotation tool.

1. dataset management:.

Annotation begins and ends with a thorough understanding of how to manage the dataset you're going to annotate. You must confirm that the tool you are contemplating will really import and support the large volume of data and file formats you need to label as a crucial element of your process. Datasets can be searched, filtered, sorted, cloned, and merged using this method.

2. Data quality control:

The quality of your data will determine the performance of your machine learning and AI models. Quality control (QC) and verification processes can be made easier with data annotation tools. Ideally, the tool will include quality control as part of the annotation process.

3. Data security:

Whether you're annotating sensitive protected personal information or your own valuable intellectual property, you'll want to keep your data secure. Tools should restrict data downloads and limit an annotator's viewing rights to data not assigned to them.

Tips for selecting a data annotation tool

1. Figure out what you're going to use it for: First and foremost, the type of data you wish to annotate as well as your work procedures will influence your tool selection. Text, image, and video can all be labeled with tools. Video labeling is possible with some image labeling software. As a result, select a tool based on your objectives.

2. What are the requirements for quality control?

Your data annotation tool should also take into account how you wish to measure and control quality. Quality control (QC) elements are included into many commercially accessible tools, and they can review, provide feedback, and correct activities.

3. Workforce training: Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourcing provider, your workforce will need access to and training to use your data annotation tool, with specific instructions particular to your use case.

3 free data annotation tools

1. computer vision annotation tool (cvat):.

CVAT is a web-based, free, open-source annotation tool that can be used to annotate image and video data for computer vision algorithms.

It has a dashboard with a list of annotation projects and tasks, as well as interpolation of shapes between keyframes, shortcuts for the most important activities, and a dashboard with a list of annotation projects and tasks. The primary tasks of supervised machine learning are supported by CVAT. Object identification, image classification, and image segmentation are all things that can be done with images.

2. Kili Technology :

Kili Technology is a labeling platform for training data. Kili Technology offers one tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI.

Kili Technology offers 3 plans for these customers, a free offer limited to 5 users and 1000 annotations per month and two paid plans from 20 000 to 500 000 annotations included as well as the addition of an external work force. Kili Technology and isahit are main partners, Kili is isahit's main technological partner, and conversely, isahit provides Kili with a diversified, competent and committed workforce.

3. Labelstudio :

Labelsudio is a flexible data labeling tool for all data types. Prepare training data for computer vision, natural language processing, speech, voice, and video models.

3 paid data annotation tools

1. labelbox.

Labelbox's training data platform is designed to assist you in improving your training data iteration loop. It's built around three main pillars: the ability to annotate data, diagnose model performance, and prioritize tasks based on your findings. By using the latest in labeling automation, you can reduce annotation costs by 50-80%, iterate 3 times quicker on your AI data to construct more performant models, and work more efficiently with data scientists, labelers, and domain experts with Labelbox.

Scale is a data platform to annotate enormous amounts of 3D sensor, picture, and video data. This data annotation tool supports several data formats and can be used for a range of computer vision applications, such as object detection, classification, and text recognition. Its advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companiesto focus on building differentiated models vs. labeling data.

V7 is an automated annotation platform that combines dataset management, image and video annotation, and autoML model training to perform labeling tasks automatically. V7 allows teams to store, manage, annotate, and automate data annotation operations in pictures, video, medical data, microscopy images, PDF and document processing, and other formats.

Want to explore more tools? We share with you this curated list of data labeling tools !

Scale your digital annotation projects with a highly trained workforce

In-house annotation can limit you in terms of volume and create some bias in annotation.

Today, data labeling companies can make all the difference in the training of your algorithms: by training and coaching a diverse, competent and committed workforce supported and challenged by a project team that follows the quality of the annotations and monitors your projects daily.

Moreover, outsourcing your annotations can also be an opportunity for the company to generate a positive social impact among the annotators! Isahit is the first and only player in the industry to offer an agile, socially responsible data labeling service powered by human intelligence.

They build, train and deploy a customized and diverse workforce on their clients' digital projects: data labeling, algorithm training, etc. while generating a real positive impact with their workforce: an additional income 5x higher than the average in their country, free trainings and a caring community to lean on. BCorp certified since 2021, isahit is revolutionizing the world of data tagging and outsourcing by making it ethical. ‍

Conclusion.

Your data annotation tool's sophistication and features have an impact on how you and your data team plan workflow, quality control, and many other areas of your data work. A tool that does not address your workforce and processes will cost you time and efficiency in the form of workarounds for features you wish were inherent to the product. Before choosing your tool, be sure to check the tool's functionality and define your needs! And if you want to scale up your annotation projects, use an external, competent and committed workforce (with ethics)!

Access a trained Workforce, managed ethically.

Ethically scale your digital annotation projects with our highly trained workforce. Access our On-Demand Workforce to get the best quality in your Dataset Labeling.

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Optimizing Data Annotation: Understanding the Process Behind the iToBoS & isahit Project

This is in fact the case in projects like iToBoS, where AI technology, with a mix between technology and Human-in-the-loop technics, is used to accurately detect melanoma. This article digs into the the isahit annotation process and workflow that we applied to iToBoS, enlightening our pivotal role as a data labeling services company, in producing quality annotations and driving successful outcomes.

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  3. 6 Key Features of Data Annotation Tools [Infographic]

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  4. A Quick Guide to Data Annotation Services and Image Labeling Services

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  5. Data Annotation Tools for Machine Learning: An Evolving Guide

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  6. Data Annotation Tools for Machine Learning: An Evolving Guide

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  3. What Is Data Annotation? Definition, Tools, Datasets [Guide]

    BLOG Machine Learning Data Annotation Tutorial: Definition, Tools, Datasets This is the only guide you need to master data annotation for machine learning. Use V7's tips and tricks to speed up your labeling and start building machine learning models that don't suck. 13 min read · September 24, 2021 Nilesh Barla PerceptronAI What is data annotation?

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    Table of Contents What Is Data Annotation? Data annotation adds metadata or labels to data to provide context and meaning. This labeling helps machine learning algorithms understand and interpret the data, ultimately enabling them to make accurate predictions or classifications.

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    Semi-automated Annotation Tools. CVAT (Computer Vision Annotation Tool): An open-source tool that offers automated annotation capabilities using pre-trained models to assist in the annotation process. MakeSense.ai: A free online tool that provides semi-automated annotation features, streamlining the process for various types of data annotation. ...

  9. The Complete Guide to Data Annotation [2024 Review]

    Data annotation is integral to the process of training a machine learning (ML) or computer vision model (CV). Datasets often include many thousands of images, videos, or both, and before an algorithmic-based model can be trained, these images or videos need to be labeled and annotated accurately. Creating training datasets is a widely used ...

  10. Data annotation: Tools, use cases, future directions [2024]

    Data annotation process here includes training data of pairs of sentences in different languages. Each pair will consist of an input sentence (in English) and an output sentence (in French). The source sentence serves as an input for the encoder, and the target is the output of the decoder. This is just the case of translation, and depending on ...

  11. Full Guide to Automated Data Annotation

    The Full Guide to Automated Data Annotation. Automated data annotation is a way to harness the power of AI-assisted tools and software to accelerate and improve the quality of creating and applying labels to images and videos for computer vision models. Automated data annotations and labels have a massive impact on the accuracy, outputs, and ...

  12. 21 Best Annotation Tools For Documents, Video, And More

    Here's a list of the seven most common types of annotation tools: Data annotation tools - these tools allow users to highlight, underline, strikethrough, or add comments to text-based content Image annotation tools - annotate images by adding text, shapes, or drawings

  13. Data Annotation

    Find the best tool for you out of a list of fast tools and frameworks for data annotation or labeling for images, videos, text (NLP) or audio. I had trouble getting a good overview of all the tools and frameworks around for data annotation so I created this list. I will try to keep it up to date.

  14. GitHub

    awesome-data-labeling. A curated list of awesome data labeling tools. Images. labelImg - LabelImg is a graphical image annotation tool and label object bounding boxes in images; CVAT - Powerful and efficient Computer Vision Annotion Tool; labelme - Image Polygonal Annotation with Python; VoTT - An open source annotation and labeling tool for image and video assets

  15. Data Annotation in 2024: Why it matters & Top 8 Best Practices

    Why it matters? What its techniques/types are? What are some key challenges of annotating data? What are some best practices for data annotation? What is data annotation? Data annotation is the process of labeling data with relevant tags to make it easier for computers to understand and interpret.

  16. Top 8 Annotation Tools (Features, Pros, Cons, Pricing)

    Top 8 Annotation Tools in 2022 1. ClickUp 2. Filestage 3. Prodigy 4. Annotate 5. PDF Annotator 6. Drawboard Projects 7. Doccano 8. lnk2Go Bonus Tool: Markup Hero Conclusion Looking for the best annotation software?

  17. Data Annotation Tools: 5 Best Tools for Annotating Data in 2023

    Lettria Lab Data Annotation Tools: 5 Best Tools for Annotating Data in 2023 Explore 2023's top data annotation tools for AI success. Dive into features, benefits, and best fit use cases of the five best tools for data annotation. by Mayank Sehgal on Aug 24, 2023 Build your custom chatbot on your own data with Lettria. Get a quick demo ->

  18. Dataset list

    A list of the best open-source annotation tools for labeling data. Use this form to add new tools to the list. Subscribe to get updates when new datasets and tools are released.

  19. 13 Absolutely Best Annotation Tools to Mark Up Your Files

    #1. Functionality #2. Annotation capabilities #3. Efficiency 13 Best Annotation Tools for 2023 #1. CELUM #2. Filestage #3. Annotate #4. Doccano #5. Ink2Go #6. PDF Annotator #7. Markup Hero #8. Zoho Annotator #9. ClickUp #10. GoVisually #11. Diigo

  20. GitHub

    Awesome data annotation. A list of tools for annotating data, managing annotations, etc. Contributing: I gladly accept additions to the lists below; please submit an issue or pull request with any updates. Just looking for a recommendation of the one best open source annotation tool?

  21. Data Annotation

    From data annotation and labeling service providers to latest research in active and semi-supervised learning. Menu. The Data Blog. ... Find the best tool for you out of a list of fast tools and frameworks for data annotation or labeling for images, videos, text (NLP) or audio. I had trouble getting a good overview of all the tools and ...

  22. Infographic

    Some of the open source tools for data annotation are: CVAT, LabelImg, Doccano, VoTT. Commercial tools Using this type of tool will require payment, however, it allows users to customize the functions to suit their need. The best commercial tools can be named: LinkedAI, Dataloop AI, Tagtog, LightTag. Freeware tools

  23. Large Language Models for Data Annotation: A Survey

    Data annotation is the labeling or tagging of raw data with relevant information, essential for improving the efficacy of machine learning models. The process, however, is labor-intensive and expensive. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to revolutionize and automate the intricate process of data annotation. While ...

  24. Ultimate list of free and paid best image annotation tools in 2023

    3. Labelme: Labelme is an open source annotation tool that you could use to process image datasets. A key feature to be noted on the tool is the "File List" option on the bottom right. This could come in handy when you have a lot of images to annotate, because it reduces the chances of you missing any images.