| Title: | OS26-4 Sign Language Recognition Algorithms Using Hybrid Techniques |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 735-740 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS26-4 |
| Author(s): | Shakir Hussain Naushad Mohamed, Hao Feng Chan, Dexter Sing Fong Leong, Wui Chung Alton Chau, Andi Prademon Yunus, Takao Ito, Zheng Cai, Xinjie Deng, Yit Hong Choo |
| Publication Date: | February 13, 2025 |
| Keywords: | Sign language recognition, pose estimation, gesture recognition, deep learning |
| Abstract: | Sign language recognition is a vital tool for enabling communication with individuals who are hearing impaired. This paper proposes a custom gesture recognition framework designed specifically for sign language interpretation. The proposed model incorporates a deep learning approach trained on a custom dataset. The system achieves robust recognition of complex gestures while maintaining efficiency. This framework emphasizes adaptability to variations in sign language styles. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS26/OS26-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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