  As the main means of communication for deaf people, sign language has a
special grammatical order, so it is meaningful and valuable to develop a
real-time translation system for sign language. In the research process, we
added a TSM module to the lightweight neural network model for the large
Chinese continuous sign language dataset . It effectively improves the network
performance with high accuracy and fast recognition speed. At the same time, we
improve the Bert-Base-Chinese model to divide Chinese sentences into words and
mapping the natural word order to the statute sign language order, and finally
use the corresponding word videos in the isolated sign language dataset to
generate the sentence video, so as to achieve the function of text-to-sign
language translation. In the last of our research we built a system with sign
language recognition and translation functions, and conducted performance tests
on the complete dataset. The sign language video recognition accuracy reached
about 99.3% with a time of about 0.05 seconds, and the sign language generation
video time was about 1.3 seconds. The sign language system has good performance
performance and is feasible.
