Data Labeling Overview: Industries & Types

Labeling is the first and the most important step in training and preparing data for machine learning.
Images/videos/texts/audio can be annotated manually or with the help of machine learning models. This is the easiest way to teach an AI to recognize the objects. There are a lot of annotation tools and services available for labeling data – the market is growing in response to demand.
It's important to provide high-quality data at this stage, because it creates a better AI model. It's a learning process – the more it learns, the more it knows. Furthermore, the knowledge must be accurate.

Where is data labeling used nowadays?

The figure below shows the industries where data labeling is applied.

IT is still the main industry for data labeling services. However, AI is unfolding in the automotive, medicine, retail, government, and banking, financial services, and insurance (BFSI) sectors worldwide.

For example, in the automotive industry, people, traffic lights, and road signs are labeled for self-driving cars. Computer vision is trained to understand the environment and situation on the road. Millions of datasets are annotated in order to teach AI to detect all these things to make self-driving cars safe.

Types of data for labeling

Images, videos, texts, and audio files are usually the input data for annotation.
Images and videos are labeled with bounding boxes, polygons, points, or lines. For text and audio files words recognition is used.

The market share between these types of input data is the following:
Images: 36%
Texts: 36%
Other: 28%*

* Source:

Manual and automatic labeling

As was mentioned above, data annotation can be manual or automatic.

People use special programs to annotate manually. They delineate objects' borders (in the case of images) and create textual notes. This process is very time-consuming. But at the same time, as long as the specialists are knowledgeable and precise, the output data will be high-quality and accurate.

Automatic annotation is a process when a computer assigns metadata (signatures or tags) to a digital image automatically using appropriate keywords to describe the image's visual content.

Existing algorithms can be divided into two categories:

  • model-based learning methods which investigate the correlation between visual characteristics and their semantic meaning using machine learning or knowledge representation models;
  • database-based models, which immediately produce a sequence of probable labels in accordance with the already annotated images in the existing database.
Labeling tools based on neural networks allow us to select objects much faster and more efficiently, in order to process a much larger number of images and to automate the bulk of manual labeling tasks, and they can be additionally trained to more accurately recognize new images.