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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- Mohammed Lawan College of Agriculture, Maiduguri, Borno State 12
- Moshood Abiola Polytechnic, Abeokuta, Ogun State 7
- Nasarawa State University, Keffi, Nasarawa State 8
- Niger Delta University, Wilberforce Island, Bayelsa State 28
- Niger State College of Education, Minna, (Affl To Usmanu Danfodiyo Uni, Sokoto) 1
- Nigeria Maritime University, Okerenkoko, Delta State 1
- Nigerian Army University, Biu, Borno State 3
- Nile University of Nigeria, Abuja 3
- Nnamdi Azikiwe University, Awka, Anambra State 98
- Northwest University, Kano, Kano State 179