An encoding and identification approach for the static sign language recognition

Abstract

Sign language identification and recognition technique is composed by the gesture images detection and the hand gestures recognition. Gesture images detection is to locate the image part of palm and fingers from the captured pictures, and rotating them to the appropriate gesture posture. Both of them are the important pre-processing for sign language identification and recognition. Lose them, the correctness rate of the sign language recognition algorithms will be dropped down to an unacceptable level. This paper presents novel processing algorithms for the gesture images detection and recognition. In the detection process, it rotates an askew gesture to right position, and to delete the elbow and forearm parts from the captured pictures. In the recognition process, it includes two phases with the model construction and the sign language identification. In the model construction phase, the static hand gesture of sign language is constructed by the Gaussian mixture model, and the unknown gesture image is identified by Gaussian model match. Based on this presented static sign language detection and recognition algorithms, the correct recognition rate is about 94% in average.