Chinese Sign Language Recognition Based on SHS Descriptor and Encoder-Decoder LSTM Model

This paper presents a novel approach to recognize isolated Chinese sign language. In order to better distinguish different hand shapes, a new Specific Hand Shape (SHS) descriptor is proposed. Based on the SHS descriptor, an encoder-decoder LSTM model is applied to achieve better sign recognition results. A specific hand shape database and an 80 words isolated Chinese sign language database are constructed using Kinect 2.0 to evaluate the proposed methods. Experimental results show the proposed SHS descriptor is more discriminative than the traditional HOG descriptor and the recognition model is more efficient than the HMM based approach.