What is Data Annotation Tool and What are Its Features?

Data Annotation Tool

Data annotation is a process that involves data labeling in order to show the result you are seeking your ML model to predict. Annotating data means to mark a data set, that is, label, tag, process, transcribe a dataset with the features you wish your ML system to recognize easily. As soon as an ML-model is deployed, the creators want to supply training data to the machine to identify some features on its own and take wise action or make a decision as a result.

When you annotate data for a machine, it serves as a training data that reveals features that will train the machine algorithms to recognize those features in the dataset that has not been annotated. Mostly, data annotation services are used in supervised or semi-supervised machine learning models.

In this blog, we will be talking about the features of data annotation tools used for the annotation and data labeling process. Before we would first like to define data annotation tools for you. Read on.

Data Annotation Tool

A data annotation tool is an on-premise, containerized or cloud-based software solution that is used to annotate training data for ML and AI models. There are organizations that follow the DIY technique when it comes to data annotation tools. Companies develop their own tools, but that is not always possible.

There are AI and ML-based companies who outsource data annotation services, without worrying about having their own tools. Data annotation tools are designed for the purpose of annotating different types of data, such as video, text, image, audio, sensor or spreadsheet data. And, these specific data type are the different types of data annotation – image annotation, video annotation, text annotation, and audio annotation services.

Let’s now move on to discuss the features of data annotation tools.

Significant Data Annotation Tool Features

Dataset Management

The first and last step of annotating data is managing the dataset to be annotated in a comprehensive way. It is essential to make sure that the tool required for the specific annotating project will accurately import and support the huge volume of data and file types you need to tag or label. The process includes searching, sorting, filtering, cloning and combining datasets.

Different tools can help get accurate results of annotations in different ways. So, it is important to know that the tool you select works for your team’s requirements. At the end of the annotation and labeling process, the output data must be saved in a secured place.

Annotation methods

One of the core features of data annotation tools is the methods and ability to apply different labels to the data. However, not every annotation tool is built based on this regard. There are several tools optimized narrowly to target specific kind of labeling, while others provide a wide mix of tools to allow different kind of use cases.

The most common type of labeling capabilities offered by data annotation tools are managing and building guidelines or ontology, such as label classes, maps, attributes and particular annotation types. We have got you some examples as well:

Video or image: polygons, bounding boxes, classification, semantic and instance segmentation, transcription, 2-D and 3-D points, or polylines.

Audio: Audio labeling tools, tagging, audio or text, time labeling.

Text: Sentiment analyses, transcription, dependency resolution, net entity relationships (NER), co-reference resolution or parts of speech (POS).

Data quality control

If you are not aware yet, the performance and working of the ML and AI models depends on the data quality. The better your data, the better will be the performance of the AI and ML models. The quality control and verification process of the data is managed by the data annotation tools. Generally, the tool has embedded QC that is a part of the annotation process.

Consider this example, how initiating issue tracking and real-time feedback is important during the annotation process. Not only this, the workflow processes like labeling consensus can also be supported. Most tools also give a quality dashboard to help the manager track and view any quality issues and assign the QC tasks to the concerned annotation team.

Workforce management

Every data annotation tool is managed by a human workforce. So, you still need humans to get along the exceptions and check quality assurance. This way, leading labeling tools will provide workforce management capabilities, like productivity analytics, task assignment and measuring time invested in each task or sub-task.

When you hire someone for data labeling services, the team will have their own technology to track the data for quality assurance. The most common and possible techniques they may use include inactivity timers, webcams, screenshots and clickstream data to recognize how they can contribute in delivering first-class data annotation results.

Most essentially, your workforce provider should be able to team with and learn the tool you wish to use. Moreover, you should be able to track the worker performance, accuracy and work quality. Getting a dashboard view is the best way for having perfect productivity of the quality of work carried out and your hired workforce.


Whether you are into annotating sensitive PPI (Protected Personal Information) or your own crucial IP (Intellectual Property), it is always essential to secure your data. The annotating tools, be it video annotation or image annotation tools, it should always restrict the annotator’s viewing rights to data and prevent them from downloading any data. Based on the way your tool is deployed, via on-premise or cloud, a data annotation tool can provide security access for the file.

For use cases falling under the regulatory compliance requirements, several tools will also log a record of annotation details, such as time, date and the annotation author. But, if you are subject to SOC 1, HIPAA, SOC 2, SSAE 16 or PCI DSS regulations, it is significant to evaluate carefully if the data annotation tool partner can provide you maintain compliance.

 In a Nutshell

If you are looking forward to outsourcing data annotation services, you must know about all types of data annotation tools and its features. From audio annotation tools to text annotation tools for your AI-based project, you can hire a professional team for the job.