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Real-Time American Sign Language Recognition from Video Using Hidden Markov Models
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In: ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-375.ps.Z (1996)
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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 realtime 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. 1 Introduction While there are many different types of gestures, the most structured sets belong to the sign languages. In sign language, each gesture already has assigned meaning, and strong rules of context and grammar may be applied to make recognition tractable. To date, most work on sign language recognition has employed expensi.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.9830
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