Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature (enhanced shape context), which can capture the spatio-temporal information well. Second, we fetch the hand regions by Kinect mapping functions and describe each frame by HOG (pre-processed by PCA). Moreover, in order to optimize predictions, rather than fixing the number of hidden states for each sign model, we independently determine it through the variation of the hand shapes. As for recognition, we propose a combination method to fuse the probabilities of trajectory and hand shape. At last, we evaluate our approach with our self-building Kinect-based dataset and the experiments demonstrate the effectiveness of our approach.
Zhang et al. (Fri,) studied this question.
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