Depth based sign language recognition system using SVM

Abstract

In this paper, we propose a sign language recognition system using an SVM (Support Vector Machine) and a depth camera. In particular, we focus on the Korean sign language. For the sign language system, we suggest two methods, one for the hand feature extraction stage and the other for the recognition stage. Hand features consist of the number of fingers, finger length, palm radius, and hand direction. To extract hand features, we use Distance Transform and a hand skeleton. This method is more accurate and faster than a traditional method that uses contours. To recognize hand posture, we develop a decision tree with hand features. For more accuracy, we use SVM to determine the threshold value in the decision tree. In the experiment results, we show that the suggested method is more accurate and faster when extracting hand features and recognizing hand postures.