Data annotation is a key part of what makes most AI algorithms work effectively. Simply put, data annotation is the process of processing unprocessed data such as speech, images, text, video, etc., so as to transform it into machine-recognizable information.
Main types of data annotation
The main types of data annotation are image annotation, speech annotation, 3D point cloud annotation and text annotation.
l?Image annotation
Image annotation is the processing of unprocessed image data, converted into machine-recognizable information, and then transported to the 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, target tracking and so on.
l?Speech annotation
Speech annotation is the annotator to the speech contained in the text information, a variety of sounds first "extracted", and then transcribe or synthesize, after the annotation of the data is mainly used for artificial intelligence machine learning, so that the computer can have the ability of speech recognition.
The common types of speech annotation are ASA speech transcription, speech cutting, speech cleaning, emotion judgment, voiceprint recognition, phoneme annotation, rhyme annotation, pronunciation proofreading and so on.
l?3D point cloud annotation
Point cloud data is generally acquired by 3D scanning equipment such as LIDAR to obtain information about a number of 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.
l?Text annotation
Text annotation is the process of feature labeling of text, which is labeled with specific data such as semantics, composition, context, purpose, emotion, and so on. Through the labeled training data, we can teach the machine how to recognize the intention or emotion implied in the text, so that the machine can better understand the language.
Common text annotation are ocr transcription, lexical annotation, named entity annotation, utterance generalization, sentiment analysis, sentence writing, slot extraction, intent matching, text judgments, text matching, text information extraction, text cleaning, machine translation and so on.
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