Sign Language Recognition Algorithms Using Hybrid Techniques
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.
Authors
- Andi Prademon Yunus (ORCID: https://orcid.org/0000-0002-1185-5763)
- Shady Mohamed (ORCID: https://orcid.org/0000-0002-8851-1635)
- Xinjie Deng
- Takao Ito (ORCID: https://orcid.org/0000-0001-6224-5204)
- Yit Hong Choo (ORCID: https://orcid.org/0000-0002-2490-1925)
- Zheng Cai (ORCID: https://orcid.org/0009-0002-1775-5752)
- Ho Yin Chan
- Wilkin Chau (ORCID: https://orcid.org/0009-0006-6239-4350)
- Dexter Sing Fong Leong
Institutions
- Hiroshima University (JP)
- Deakin University (AU)
- Telkom University (ID)
Publication Details
- Journal
- Proceedings of International Conference on Artificial Life and Robotics
- Published
- 2025-02-13
- DOI
- https://doi.org/10.5954/icarob.2025.os26-4
- Primary Topic
- Hand Gesture Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00