What does data annotation do?

Data annotation is the key link for most artificial intelligence algorithms to run effectively. Simply put, data annotation is a process of processing unprocessed voice, pictures, texts, videos and other data, thus transforming them into machine-readable information.

main types of data annotation

The types of data annotation are mainly image annotation, voice annotation, 3D point cloud annotation and text annotation.

l? Image annotation

Image annotation is

processing unprocessed image data, converting it into machine-readable information, and then transferring it to artificial intelligence algorithms and models to complete the call.

The common image annotation methods include semantic segmentation, rectangular box annotation, polygon annotation, key point annotation, point cloud annotation, 3D cube annotation, 2D/3D fusion annotation, target tracking and so on.

l? Phonetic annotation

Phonetic annotation means that the annotator "extracts" the text information and various sounds contained in the voice, and then transcribes or synthesizes them. The annotated data is mainly used for artificial intelligence machine learning, so that the computer can have voice recognition ability.

The common phonetic annotation types include ASA phonetic transcription, phonetic cutting, phonetic cleaning, emotional judgment, voiceprint recognition, phoneme annotation, prosodic annotation, pronunciation proofreading, etc.

l? 3D point cloud labeling

Generally, point cloud data is a multi-dimensional complex data set, which is obtained by 3D scanning equipment such as laser radar, including XYZ position information, RGB color information and intensity information.

3D point cloud data can provide abundant geometric, shape and scale information, and it is not easily affected by the change of illumination intensity and other objects, so it can well understand the surrounding environment of the machine.

Common 3D point cloud annotation types include 3D point cloud target detection annotation, 3D point cloud semantic segmentation annotation, 2D3D fusion annotation, and point cloud continuous frame annotation.

l? Text tagging

Text tagging is a process of feature tagging of text, which is tagged with specific data such as semantics, composition, context, purpose, emotion, etc. By tagging good training data, we can teach the machine how to identify the intention or emotion implied in the text, so that the machine can better understand the language.

Common text tagging includes ocr transliteration, part-of-speech tagging, named entity tagging, sentence generalization, sentiment analysis, sentence writing, slot extraction, intention matching, text judgment, text matching, text information extraction, text cleaning, machine translation and so on.

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