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Real-time American sign language recognition using desk and wearable computer based video

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3 Author(s)
Starner, T. ; Media Lab., MIT, Cambridge, MA, USA ; Weaver, J. ; Pentland, A.

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

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:20 ,  Issue: 12 )

Date of Publication:

Dec 1998

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