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

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Real-time american sign language recognition using desk and wearable computer based video (1998)

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by Thad Starner , Joshua Weaver , Alex Pentland

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	627 - 26 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.

Keyphrases

wearable computer real-time american sign language recognition real-time hidden markov model-based system percent word accuracy second system percent accuracy single camera 40-word lexicon unrestricted grammar sentence-level continuous american sign language first system

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