一种基于滑动窗口分割的中国手语识别系统

听力障碍者在全世界残疾人群体中占有较大的比重.他们能通过手语与健全人交流，但因手语不被大众所掌握，导致彼此交流存在较大障碍.为此提出了一种基于滑动窗口分割（SSW）的连续中国手语识别系统来实现手语自动识别.SSW系统将通过滑动窗口选取出来的手语信号平均分割，依次删去其中一组数据，从而得到新的数据，输入手语识别神经网络进行训练，得出单个手语单词手势预测值，最后运用基于阈值的多投票策略对识别出的预测值进行判断，得出识别结果.SSW系统在对20名志愿者采集的30条手语语句上进行训练，结果显示，所提SSW系统自动识别手语的平均准确率在测试集上达到83.9%，较长短期记忆网络模型提高了16.7%.

A Split Sliding Window-Based Continuous Chinese Sign Language Recognition System

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.