Visual Recognition of American Sign Language Using Hidden Markov Models.

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 an HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers. 1 Introduction There has been a resurging interest in recognizing human hand gestures. While there are many interesting domains, one of the most structured sets of gestures are those belonging to any of the several 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 expensive wired "datagloves" which the user must wear [19]. In addition, these systems have mostly concentrated on fin...