The methods of data annotation are image annotation, speech annotation, 3D point cloud annotation and text annotation.

Image annotation

Image annotation is the processing of unprocessed image data, converted into machine-recognizable information, and then transported to artificial intelligence algorithms and models to complete the call.

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, and target tracking.

Speech annotation

Speech annotation is the annotator to the voice contains text information, a variety of sounds first "extracted", and then transcribed or synthesized, after the annotation of the data is mainly used for artificial intelligence machine learning, so that the computer can have speech recognition capabilities.

The common types of speech annotation are ASA speech transcription, speech cutting, speech cleaning, emotion judgment, voiceprint recognition, phoneme annotation, rhyme annotation, pronunciation proofreading, etc. The data is then used for AI machine learning so that the computer can have the ability to recognize speech.

3D point cloud annotation

Point cloud data is generally acquired by 3D scanning equipment such as LIDAR to obtain information of several points in space, including XYZ position information, RGB color information and intensity information, etc., which is a multi-dimensional and complex data collection.

3D point cloud data can provide rich geometric, shape and scale information, and is not easily affected by changes in light intensity and other objects such as occlusion, which can provide a good understanding of the machine's surroundings.

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

Text annotation

Text annotation is the process of tagging text with specific semantic, compositional, contextual, purposeful, emotional and other data labels, through the labeled training data, we can teach the machine how to recognize the intent or emotion implied in the text, so that the machine can better understand the language.

Common text annotations are ocr transcription, lexical annotation, named entity annotation, utterance generalization, sentiment analysis, sentence writing, slot extraction, intent matching, text judgment, text matching, text information extraction, text cleaning, machine translation, and so on.