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
- Sokoto State University, Sokoto, Sokoto State 42
- St. Albert The Great Major Seminary, Abeokuta. (affl. To University of Benin) 1
- Sule Lamido University, Kafin Hausa, Jigawa State 4
- Tai Solarin University of Education, Ijagun, Ogun State 18
- Tansian University, Oba, Anambra State 1
- Taraba State University, Jalingo, Taraba State 32
- Temple-Gate Polytechnic, Osisioma, Abia State 1
- The Oke-Ogun Polytechnic, Saki, Oyo State 6
- The Polytechnic, Ibadan, Oyo State 13
- THOMAS ADEWUMI UNIVERSITY, OKO-IRESE, KWARA STATE 1