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
design implementation multilingual language recognition system
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