Sign language recognition is the computer-vision and sequence-modelling task of translating the manual and non-manual gestures of a signed language into text or speech. It must model hand shape, motion trajectory, facial expression, and grammatical structure that differs fundamentally from spoken languages. It is an accessibility-focused application that builds on robust hand tracking and temporal recognition.
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- Modern systems combine pose-estimation backbones with temporal models such as transformers or CTC-trained recurrent networks to handle continuous signing and coarticulation. Challenges include signer variation, limited annotated corpora, and capturing the simultaneous non-manual markers that carry grammatical meaning.