Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
Journal Home Volume Issue |
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Issue |
Volume-11,Issue-7 ( Jul, 2023 ) | |||||||||
Paper Title |
Light Weight Network Architecture for Sign Language Recognition | |||||||||
Author Name |
Borjiunn Hwang, Chiaowen Kao, Huihui Chen, Yuanchia Lin | |||||||||
Affilition |
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Pages |
9-12 | |||||||||
Abstract |
Developing sign language recognitionsystems using deep learning models has become a prominent trend. However, the meaning conveyed by sign language gestures varies across different countries, making it difficult to share open sign language datasets. Additionally, the complex architecture of deep learning-based sign language recognition networks presents challenges in achieving real-time translation and localizing the technology for practical use. To address these challenges, this paper proposes a lightweight sign language recognition network architecture that optimizes the existing MobileNetV3+LSTM architecture. The goal is to reduce the network model size, decrease computational complexity, and maintain accuracy. The proposed model was evaluated using a self-collected dataset consisting of 14 types of Taiwanese daily life sign language gestures. Compared to other models, the proposed model achieved a 99.2% accuracy rate while reducing complexity by 72%. Keywords - Sign Language Recognition, Taiwanese Daily Life Sign Language, Lightweight, Edge Device. | |||||||||
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