Visual Recognition of American Sign Language Using Hidden Markov Models
نویسنده
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 for visual recognition of complex, structured hand gestures such as are found in sign language. We describe an HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the ngers.
منابع مشابه
Real-Time American Sign Language Recognition from Video Using Hidden Markov Models
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 accu...
متن کاملMAN-MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL
Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape inf...
متن کاملVisual Sign Language Recognition Based on HMMs and Auto-regressive HMMs
A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don’t have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is...
متن کاملReal-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 ...
متن کاملDynamic Gesture Recognition using Transformation Invariant Hand Shape Recognition
In this thesis a detailed framework is presented for accurate real time gesture recognition. Our approach to develop a hand-shape classifier, trained using computer animation, along with its application in dynamic gesture recognition is described. The system developed operates in real time and provides accurate gesture recognition. It operates using a single low resolution camera and operates i...
متن کامل