A Vision-Based Taiwanese Sign Language Recognition

This paper presents a vision-based continuous sign language recognition system to interpret the Taiwanese Sign Language (TSL). The continuous sign language, which consists of a sequence of hold and movement segments, can be decomposed into non-signs and signs. The signs can be either static signs or dynamic signs. The former can be found in the hold segment, whereas the latter can be identified in the combination of hold and movement segments. We use Support Vector Machine (SVM) to recognize the static sign and apply HMM model to identify the dynamic signs. Finally, we use the finite state machine to verify the correctness of the grammar of the recognized TSL sentence, and correct the miss-recognized signs.