Real-time american sign language recognition using desk and wearable computer based video
T Starner, J Weaver, A Pentland - IEEE Transactions on pattern …, 1998 - ieeexplore.ieee.org
T Starner, J Weaver, A Pentland
IEEE Transactions on pattern analysis and machine intelligence, 1998•ieeexplore.ieee.orgWe 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.
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.
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.
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