[Retracted] Sign Language Recognition and Application Based on Graph Neural Networks: Innovative Integration in TV News Sign Language

Abstract

With the rapid development of information technology, sign language recognition plays an extremely important role in the communication among people with hearing impairments. Especially in the context of television news, the real-time and accuracy of sign language translation are very important. Traditional sign language translation technology faces challenges such as low accuracy of gesture recognition and poor real-time performance, which makes it difficult to meet the translation needs of daily complex news content. This paper proposes a sign language recognition method based on graph neural network (GNN). By constructing a graph structure of gesture nodes and joint connections, GNN can capture the relationship between gestures and efficiently transfer learning information. Through comparative experiments with traditional convolutional neural networks (CNN), the advantages of GNN in sign language recognition are proved, especially in the application of news broadcasting, which significantly improves the real-time and accuracy of sign language translation. Future research will focus on optimizing the generalization ability of the model and broadening its applicability to more languages and scenarios.