IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

en Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification Deep Learning Deep Learning Research Paper Research Paper This paper develops a robust and efficient method for the classification of Vietnamese Sign Language gestures. The study focuses on leveraging deep learning techniques, specifically a Graph Convolutional Network (GCN), to analyze hand skeletal points for gesture recognition. The Vietnamese Sign Language custom dataset (ViSL) of 33 characters and numbers, conducting experiments to validate the model's performance, and comparing it with existing architectures. The proposed approach integrates multiple streams of GCN, based on the lightweight MobileNet architecture. The custom dataset is preprocessed to extract key skeletal points using Mediapipe, forming the input for the multiple GCN. Experiments were conducted to evaluate the proposed model's accuracy, comparing its performance with traditional architectures such as VGG and ViT. The experimental results highlight the proposed model superior performance, achieving an accuracy of 99.94% test on the custom ViSL dataset, reach accuracy of 0.993% and 0.994% on American Sign Language (ASL) and ASL MINST dataset, respectivly. The multi-stream GCN approach significantly outperformed traditional architectures in terms of both accuracy and computational efficiency. This study demonstrates the effectiveness of using multi-stream GCNs based on MobileNet for ViSL recognition, showcasing their potential for real-world applications. Deep Learning, Graph Convolutional Network, Vietnamese Sign Language, Skeletal Join Points, Classification. 106 117 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5584-1&slc_lang=en&sid=1 Manh-Hung Ha hunghm@vnu.edu.vn 1800319475328460017397 1800319475328460017397 Yes Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam. Duc-Chinh Nguyen 22075057@vnu.edu.vn 1800319475328460017398 1800319475328460017398 No Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam. Thai-Kim Dinh huy.leminh@phenikaa-uni.edu.vn 1800319475328460017399 1800319475328460017399 No Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam. Tran Tien-Tam tamtt@vnu.edu.vn 1800319475328460017400 1800319475328460017400 No Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam. Do Tien Thanh thanhdo@vnuis.edu.vn 1800319475328460017401 1800319475328460017401 No Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam. Oscal Tzyh-Chiang Chen oscal@ee.ccu.edu.tw 1800319475328460017402 1800319475328460017402 No Faculty of Applied Sciences, International School, Vietnam National University, Hanoi 100000, Vietnam.