Scientific Development Research

With the rapid development of artificial intelligence and computer vision technology, sign language recognition has become an important direction of human-computer interaction and barrier-free communication research. Traditional sign language recognition methods often rely on single modal data, such as image or video, which has problems such as information loss and recognition accuracy limitation. Multi-modal data fusion combined with vision, depth information, EMG, IMU, etc., can enrich semantic expression and improve recognition accuracy. This paper discusses how deep neural networks combine multi-modal data to improve sign language recognition performance, analyzes the key technologies, challenges and application prospects, in order to provide reference for the optimization of intelligent sign language translation system.

Multi-modal data; Deep neural network; Sign language recognition; Applied research