Hand Motion Recognition for the Vision-based Taiwanese Sign Language Interpretation

In this paper we present a system to recognize the hand motion of Taiwanese Sign Language (TSL) using the Hidden Markov Models (HMMs) through a vision-based interface. Our hand motion recognition system consists of four phases: construction of color model, hand tracking, trajectory representation, and recognition. Our hand tracking can accurately track the hand positions. Since our system is recognized to hand motions that are variant with rotation, translation, symmetric, and scaling in Cartesian coordinate system, we have chosen invariant features which convert our coordinate system from Cartesian coordinate system to Polar coordinate system. There are nine hand motion patterns defined for TSL. Experimental results show that our proposed method successfully chooses invariant features to recognition with accuracy about 90%.