4 types of data annotation tools and their uses

Friday, 08 July 2022 11:03

4 types of data annotation tools and their uses

By Guest Writer

GUEST OPINION: Are you planning on buying or building a data annotation tool for your AI project? Find out about your options and their corresponding use cases.

Data annotation is the process of assigning tags or labels to a digital object (image, video, audio, text, etc.). It’s an essential component of artificial intelligence (AI) and machine learning (ML) as it properly allows practitioners to categorize data according to their use cases. While humans can, in theory, annotate data alone, AI projects often require large amounts of data. Hence, the demand for data exceeds a human’s annotation capabilities. This is where specific tools come into play. 

Data annotation tools allow AI/ML practitioners to automate this process, minimize the need for human input, and maximize the rate at which practitioners annotate data. There are different types of data annotation tools on the market, and their use cases vary. This article covers the four data annotation tools types and uses: computer vision, natural language processing, open-source, and freeware. Read on to learn more.

1. Computer vision (CV) tools 

Computer vision is a field of AI that creates algorithms and systems that can derive valuable information from visual data. While it has a more extensive scope than data annotation, tools that possess CV capabilities are crucial for AI projects that rely on data derived from multimedia files. 

Examples of such projects include self-driving cars, which often need large amounts of data from videotapes taken by the vehicle. CV tools have two main capabilities, including the following:

• Image annotation is the ability to annotate images using various techniques, such as bounding boxes, polygons, 2D and 3D points, polylines, and segmentation. 
• Video annotation allows the tool to annotate videos using the same technique as an image annotation. A video annotation tool may also use other techniques, like tracking objects in each frame. 

Specific CV tools support automation, making it suitable for AI projects that have large datasets. With CV, it can accelerate the data annotation process a lot. But of course, there are tools on the market that lack this feature, so it’s best to choose a CV tool after taking the appropriate considerations. 

2. Natural language processing (NLP) tools 

Natural language processing (NLP) is yet another field of AI with the same concept as computer vision. It creates algorithms that can analyze and derive valuable information from digital objects. However, rather than visual data, it focuses on texts and transcriptions. Moreover, it has capabilities crucial for AI projects that depend on text data. These include tasks like machine translation, chatbots, and text-to-speech and speech-to-text converters. 

NLP and CV tools are the two types of data annotation tools according to data type. CV focuses on multimedia, while NLP focuses on texts. But, of course, there are other types of data annotation tools based on accessibility.

3. Open-source tools 

An open-source data annotation tool is software that gives users the permission and privilege to modify the source code, hence the name. That means you can customize the features and make changes as you see fit. Its primary use allows developers to create the most suitable data annotation tool for your needs. This level of control and customizability can mean you can create tools from scratch for your projects.

Open-source data annotation tools are convenient in large-scale AI projects like self-driving cars and self-service retail stores. This is because these projects often require top-notch security. Since you have access to the software’s source code, you and other developers in the community can quickly maximize the tool’s security by incorporating specific features.

Perhaps the main downside of this type of tool is it necessitates commitment. Since you technically own the tool, you must invest time and money in its improvement. 

4. Freeware tools 

As the name suggests, a freeware data annotation tool is a program you can download, install, use, and even distribute at no cost. You can also use it in any way you want, meaning you can customize it through the source code. In short, all freeware tools are open source.

Yes, it seems too good to be true, but it is. Unfortunately, however, freeware annotation tools are pretty rare, and you’ll find that most of them are rather lacklustre. Of course, this is understandable, as the original developer has almost nothing to gain, unlike some open-source tools that require payment. Nevertheless, with the help of features developed by the community, freeware tools can also prove to be handy. 

Closing thoughts 

Remember that you won’t need all these tools for your system. It depends on the use cases of your AI project. The AI of a self-service retail store, for example, wouldn’t need an NLP tool as its primary goal is to scan images of the products. This is yet another reason why understanding the different types of data annotation tools is crucial. It helps you choose the most suitable tool for your purposes and allows you to save money by investing only in those that you need.

References

IBM, What Is Computer Vision?

IBM, Natural Language Processing (NLP)

Saxena, Mohit, Opensource Software vs Free Software vs Freeware Software, Knoldus, 1 December 2021

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