Labeling and curation are used to create large volumes of annotated data, which is then used to train machines and make them functional for AI-based models. The various Digital Labeling Content Types that we work with include:
For AI systems and models, image labeling is used to help them interpret an image for facial recognition, robotic vision, etc. When labeling an image; captions, keywords and identifiers are used as attributes. These parameters help AI-powered models to identify and understand images and learn autonomously.
Compared to image data, audio data has more dynamics/factors such as language, dialects, speaker demographics, and more. It also has non-verbal cues like breaths, silence and background noise. All these parameters need to be identified and labelled through audio labeling.
Through video labeling with metadata, the machine can seamlessly classify and evaluate a high volume of videos on various parameters. Compared to images that are still, videos have different objects in motion and each of these is called a frame. The video labeling process includes polygons, keypoints and bounding boxes to annotate different objects (in each frame).
While images and videos are straightforward, text/file has a lot of semantics (humour, sarcasm) that cannot be understood by the machines to a precise level. Here, a more refined labeling process is involved including text categorization (sentences and phrases are tagged/classified), semantics tagging, entity tagging, and intent tagging.
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