Replaced by TR466
Real-Time American Sign Language Recognition from Video
Using Hidden Markov Models
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375An earlier version appeared ISCV '95
Real-Time American Sign Language Recognition from Video
Using Hidden Markov Models
Thad Starner and Alex Pentland
Room E15-383, The Media Laboratory
Massachusetts Institute of Technology
20 Ames Street, Cambridge MA 02139
thad,sandy@media.mit.edu
Abstract:
Hidden Markov models (HMM's) have been used prominently and
successfully in speech recognition and, more recently, in handwriting
recognition. Consequently, they seem ideal for visual recognition of
complex, structured hand gestures such as are found in sign language.
We describe two experiments that demonstrate a real-time HMM-based
system for recognizing sentence level American Sign Language (ASL)
without explicitly modeling the fingers. The first experiment tracks
hands wearing colored gloves and attains a word accuracy of 99%. The
second experiment tracks hands without gloves and attains a word
accuracy of 92%. Both experiments have a 40 word lexicon.
Thad E Starner
Sun Apr 7 22:38:14 EDT 1996