Real-time american sign language recognition using desk and wearable computer based video (1998)
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| Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
| Citations: | 367 - 20 self |
BibTeX
@ARTICLE{Starner98real-timeamerican,
author = {Thad Starner and Joshua Weaver and Alex Pentland},
title = {Real-time american sign language recognition using desk and wearable computer based video},
journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
year = {1998},
volume = {20},
number = {12},
pages = {1371--1375}
}
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Abstract
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.
