Sign Language Recognition System Based on Weighted Hidden Markov Model

Sign language recognition (SLR) plays an important role in communication between deaf and hearing society. However, the recognition result is still worse for signer independent recognition. The reason is that there exists large variation between the signs from different subjects. In this paper, weighted hidden markov model (HMM) is proposed to deal with the variation. Unlike traditional HMM, WHMM assigns each sign samples with different weights. For the sign sample with big variation, the sample weight is big accordingly. Furthermore, we utilize Kinect to produce robust sign features to improve recognition rate. Our system is evaluated on one Chinese sign language dataset of 156 isolated sign words. Experimental result shows our proposed method outperforms other methods with a high recognition rate of 94.74%.