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As the main means of communication for deaf people, sign language has aspecial grammatical order, so it is meaningful and valuable to develop areal-time translation system for sign language. In the research process, weadded a TSM module to the lightweight neural network model for the largeChinese continuous sign language dataset . It effectively improves the networkperformance with high accuracy and fast recognition speed. At the same time, weimprove the Bert-Base-Chinese model to divide Chinese sentences into words andmapping the natural word order to the statute sign language order, and finallyuse the corresponding word videos in the isolated sign language dataset togenerate the sentence video, so as to achieve the function of text-to-signlanguage translation. In the last of our research we built a system with signlanguage recognition and translation functions, and conducted performance testson the complete dataset. The sign language video recognition accuracy reachedabout 99.3% with a time of about 0.05 seconds, and the sign language generationvideo time was about 1.3 seconds. The sign language system has good performanceperformance and is feasible.

A two-way translation system of Chinese sign language based on computer vision