Referenced Items are not available for this document.
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
- Page(s):
- 1371 - 1375
- ISSN :
- 0162-8828
- INSPEC Accession Number:
- 6131787
- Digital Object Identifier :
- 10.1109/34.735811
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Dec 1998
- Sponsored by :
- IEEE Computer Society
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