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

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.

But what 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.

Key Features:

• Collaborative Environment: Multiple annotators can work simultaneously, speeding up the annotation process.
• Flexibility: Kili supports a variety of data types, from text to images.

Advantages:

• 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,

Disadvantages:

• 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.

Key Features:

• 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.

Advantages:

• 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.

Disadvantages:

• 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.

Key Features:

• 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.

Advantages:

• 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.

Disadvantages:

• 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.

Key Features:

• 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.

Advantages:

• 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.

Disadvantages:

• 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!

Conclusion

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!