Real-Time American Sign Language Recognition from Video Using Hidden Markov Models
نویسندگان
چکیده
Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal f o r visual recognition of complex, structured hand gestures such as are found in sign language. We describe a real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.
منابع مشابه
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
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% word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98% accuracy (97% with an unrestricted ...
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