Design and Implementation of Multilingual Sign Language Recognition System
Student: Ayobami Joshua Adeleke (Project, 2025)
Department of Computer and Information Science
Bamidele Olumilua University of Edu. Science and Tech. Ikere Ekiti, Ekiti State
Abstract
This project develops a real-time multilingual sign language recognition system using YOLO and TensorFlow, achieving 99% accuracy in detecting gestures across languages like ASL and BSL. Despite hardware and scope limitations, it shows strong potential to enhance accessibility for the Deaf community.
Keywords
Multilingual Sign Language Recognition
YOLO
TensorFlow
Real-Time Gesture Detection
Accessibility
Deaf Communication
Artificial Intelligence
Computer Vision
Inclusivity
Human-AI Interaction
For the full publication, please contact the author directly at: adeleke.0492@bouesti.edu.ng
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Institutions
- Federal Polytechnic, Mubi, Adamawa State 20
- Federal Polytechnic, Nasarawa, Nasarawa State 66
- Federal Polytechnic, Nekede, Imo State 56
- Federal Polytechnic, offa, Kwara State 20
- Federal Polytechnic, Oko, Anambra State 8
- Federal School of Biomedical Engineering, (LUTH), Idi-Araba, Lagos State 1
- Federal School of Surveying, Oyo, Oyo State 7
- Federal University of Agriculture, Abeokuta, Ogun State 19
- Federal University of Petroleum Resources, Effurun, Delta State 83
- Federal University of Technology Akure, Ondo State 24