Real-time American sign language recognition using desk and wearable computer based video
Thad E. Starner, Joshua Weaver, Alex Pentland · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998
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 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.