Design and Implementation of Multilingual Sign Language Recognition System
Student: Isaac Ayomilekan Ayodele (Project, 2025)
Department of Computer and Information Science
Bamidele Olumilua University of Edu. Science and Tech. Ikere Ekiti, Ekiti State
Abstract
The Multilingual Sign Language Recognition System addresses communication barriers faced by the hearing and speech-impaired community, especially in multilingual contexts. By leveraging advanced deep learning techniques, the YOLO algorithm for real-time gesture detection and TensorFlow for classification, this system focuses on recognizing hand gestures across multiple sign languages such as ASL and BSL. The study achieved a detection accuracy of 99%. Despite limitations like dependency on high-performance hardware and exclusion of facial expressions, the project demonstrates significant potential as an assistive technology. Recommendations include expanding dataset diversity, integrating additional recognition capabilities, and optimizing for edge devices to enhance accessibility and scalability.
Keywords
For the full publication, please contact the author directly at: isaac.0582@bouesti.edu.ng
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Institutions
- Covenant Polytechnic, Aba, Abia State 1
- Covenant University, Canaan Land, Ota, Ogun State 4
- Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State 2
- Crescent University, Abeokuta, Ogun State 1
- Cross Rivers University of Technology, Calabar, Cross Rivers State 142
- Delta State Polytechnic, Ogwashi-Uku, Delta State 11
- Delta State Polytechnic, Otefe, Delta State 12
- Delta State University, Abraka, Delta State 139
- Ebonyi State University, Abakaliki, Ebonyi State 17
- Edo University, Iyamho, Edo State 10