We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92% word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98% accuracy (97% with an unrestricted grammar). Both experiments use a 40 word lexicon.
T. Starner et al. also used HMM for the real time recognition of a sign language using a single camera to track unadorned hands of a user REF .
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE PAMI ’98
Computer Science