A RECOGNITION METHOD OF SINGLE-HAND DYNAMIC FINGER WORDS IN CSL

In this paper, a novel recognition method of single-hand dynamic finger words of Chinese Sign Language (CSL) based on multi-feature matching and D-S theory is introduced, which uses the dataglove CAS-Glove as the input device and partitions the motion of each finger joints into a fuzzy state. In this method, each word is represented by the vector of initial appearance, probability distribution of each state and the vector of the periodic property. The basic probability assignments of candidate words are obtained by feature match and fused to achieve the best words by D-S theory. The results of experiments show that this method has higher recognition rate and better real-time property, so the effectivity of this method is confirmed.