Research on Cantonese Sign Language Generation Based on Convolutional Neural Networks

Cantonese sign language, due to its unique grammatical structure and gesture expressions, differs significantly from universal sign language, causing many communication difficulties for the deaf and mute in Cantonese-speaking regions. To address this, this paper proposes a method for generating Cantonese sign language based on Convolutional Neural Networks (CNNs). This method leverages deep learning techniques to automatically learn the features of Cantonese sign language, generating fluent, natural, and highly adaptable sign language movements. Based on existing Cantonese sign language data resources, an efficient network model is designed to improve the quality of generation, and computer vision techniques are integrated to optimize the generation process. Experimental results demonstrate that the proposed method shows great potential in terms of accuracy and naturalness in generation. It will provide more convenient communication methods for the deaf and mute in Cantonese-speaking regions and offer an important reference for the development of sign language generation technologies.