We are surrounded by millions of images on the Internet every day, and although they have no verbal description, the human brain does not hesitate to recognize the various objects in a photo, classify them, and form a common understanding of what is in the photo. Unfortunately, this is not easy for a computer to do, and it requires non-standard Machine Learning approaches.
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. A good example for annottaion can be used automatic text description generation for photos.
The task of generating automatic text consists of two main parts: understanding what is in the image, as well as a description of these things in natural language. Object detection and recognition algorithms are used to understand the content of the image. After receiving this information, it is important to understand the type and location of objects, how they interact with each other and form sentences. The formation of a sentence requires from us not only a semantic understanding of what is in the photo, but also syntactically competent design.
Behind the AI systems that give sight to machines, you’ll find a computer vision annotation tool. These tools are the key to taking raw image data and turning it into training data for machine learning models. Annotation tools help autonomous vehicles to recognize traffic conditions, warehouse robots to differentiate stock, and delivery drones to navigate to addresses.
Within computer vision, annotation tools are used for a variety of different applications. Although facial recognition, object detection, and medical imaging all fit under the umbrella of computer vision, each requires a different kind of annotation to achieve its goals. 
Some key players operating in the data annotation tools market include Appen Limited; Cogito Tech LLC; Deep Systems; Labelbox, Inc.; LightTag; Playment Inc.; CloudFactory Limited; Clickworker GmbH; Alegion; Figure Eight Inc.; Amazon Mechanical Turk, Inc.; amongst others. 
The global data annotation tools market size was valued at USD 390.1 million in 2019 and is projected to register a CAGR of 26.9% from 2020 to 2027. Increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors is a key factor driving the market. These tools enable users to enhance the value of data by adding attribute tags to it or labeling it.
Data annotation is expected to play a major role in enhancing the applications of AI in the healthcare sector. AI-backed machines use machine vision or computer vision in medical imaging data technologies to sense patterns and identify possible injuries, which assists medical practitioners in automatically generating reports after the individual is examined. The database of CT scan, MRI, and X-Ray images can be easily screened by the AI to determine various injuries. Data annotation tools help train AI systems in differentiating data obtained from normal and injured medical images to generate the final reports of the examined individuals.