Recognizing Chinese Sign Language Based on Deep Neural Network

Gesture recognition is ongoing attention in the field of human computer interaction (HCI). With development of deep neural network technology in computer vision, more complex sign languages are possible to recognize but, the research on Chinese language (CSL) recognition remain in discussion. Here we have performed our collected dataset and proposes a new solution to recognize CSL, and further insight on preliminary verification on CSL recognition using 2D image. This paper attempts to reduce the adverse impact of dataset itself on the image recognition network using continuously improved technical method. Present study addresses the following: 1) Due to the lack of the CSL image dataset, we made a CSL dataset and used it in the following experiments to verify the usability of the dataset. 2) Using a self-made dataset, we combined the method of hand skeletal gesture recognition to reduce the impact of the gesture overlap and improve recognition accuracy. Finally, a network model was trained and tested on self-made dataset which include some overlapping gestures that are difficult to recognize and achieved the accuracy rate of 0.9324. 3) Put forward the idea of continuing the experiment to improve dataset and using fuzzy semantic recognition for trying to solve the time-domain problem of dynamic sign language recognition which needs linguistic studies.