A Model For Real Time Sign Language Recognition System

This paper proposes a real time approach to recognize gestures of sign language. The input video to a sign language recognition system is made independent of the environment in which signer is present. Active contours are used to segment and track the non-rigid hands and head of the signer. The energy minimization of active contours is accomplished by using object color, texture, boundary edge map and prior shape information. A feature matrix is designed from segmented and tracked hand and head portions. This feature matrix dimensions are minimized by temporal pooling creating a row vector for each gesture video. Pattern classification of gestures is achieved by implementing fuzzy inference system. The proposed system translates video signs into text and voice commands. The training and testing of Fuzzy Inference system is done using Indian Sign Language. Our data base has 351 gestures with gesture repeated 10 times by 10 different users. We achieved a recognition rate of 96% for gestures in all background environments.