%0 Journal Article
%A DAI Hai-peng
%A LIU Peng
%A MA Xiao-di
%A WANG Qing-shan
%A WANG Xin-yan
%T A Split Sliding Window-Based Continuous Chinese Sign Language Recognition System
%D 2021
%R 10.13190/j.jbupt.2021-001
%J Journal of Beijing University of Posts and Telecommunications
%P 48-54
%V 44
%N 5
%X A large proportion of the world's disabled population is accounted for the individuals with hearing impairment which can communicate with people through the sign language. However, sign language is not mastered by the public, and there are still big obstacles between the individuals with hearing impairment and the normal people. A continuous Chinese sign language recognition system based on split sli-ding window (SSW) to realize automatic sign language recognition is proposed. The SSW system divides the sign language signal selected through the sliding window, and deletes one group of data to get new data in the original order, which is inputted to the sign language recognition neural network for training to obtain the gesture prediction value of a single sign language word. Finally, the majority voting strategy based on threshold is used to judge the identified prediction values. The SSW system is trained on 30 sign language sentences collected by 20 volunteers. The results show that the average accuracy of the SSW system reachs 83.9% on the test dataset, which is 16.7% higher than the long short-term memory model.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-001