RECOGNIZING VIETNAMESE SIGN LANGUAGE USING DEEP NEURAL NETWORKS

Tóm tắt

Vietnamese sign language plays a pivotal role in enabling effective communication among deaf and hard-of-hearing communities throughout Vietnam. In this study, we propose a deep learning-based recognition system that leverages MediaPipe to accurately extract hand landmarks from video sequences. These landmarks are then processed by an architecture, either a convolutional neural network or a long short-term memory network enhanced with an attention mechanism (such as additive or multi-head attention), to selectively highlight salient temporal patterns in sign gestures. To support robust training and evaluation, we compiled and meticulously annotated a comprehensive dataset of Vietnamese sign language gestures. Experimental results demonstrate that the proposed model attains a remarkable recognition accuracy of 99.51%, outperforming baseline approaches. The system’s real-time performance and high precision highlight its potential as the basis for practical assistive communication tools, paving the way for further research in sign language processing and cross-cultural gesture recognition applications within the Vietnamese context