Abstract: Sign language is an important means of communication among people with hearing impairment.Through sign language recognition,patients can communicate with normal people without barriers.With the development of deep learning technology,various sign language recognition technologies have also developed,but the existing sign language recognition technologies often cannot complete the task of continuous sign language recognition.Therefore,this paper proposes a continuous sign language re-cognition method based on graph convolution network(GCN) and connectionist temporal classification of neural network classification/attention( CTC/Attention),which extracts features from the space dimension and time dimension,respectively.The mechanism of spatial attention is blended in among them,assigning weight given to bone point,thereby highlight the effective spatial characteristics and to realize continuous sign language recognition.This method can realize sequence alignment and contextual semantic modeling of continuous sign language sentence translation.Firstly,data of sign language action bone points are collected based on MediaPipe framework,and a dataset of skeletal key point in Chinese sign language is built based on this.A dynamic chiral word recognition method based on Spatio-Temporal graph convolutional network(ST-GCN) is designed.Finally,a method based on GCN and CTC/Attention code network is proposed to realize continuous sign language sentence recognition.In the case of limited datasets,the proposed method is evaluated on the self-built skeletal point dataset SSLD,the experimental results show that,the average continuous sign language recognition accuracy reaches 94.41%,and the model has been proved to have good sign language recognition ability.

Key words: Continuous sign language recognition, Graph convolutional network, Temporal class classification based on neural network, MediaPipe frame, Skeletal key point, Spatio-temporal graph based neural network