Citation

Abdulrahim, Khairi and Khahar, Abdul Hakim (2026) Quranic Malaysian Sign Language Detection Using Yolo for Real-Time Translation. Journal of Engineering Technology and Applied Physics, 8 (2). p. 157. ISSN 2682-8383

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Abstract

This study presents the development of a real-time Quranic Malaysian Sign Language (MySL) recognition system designed to enhance accessibility to Quranic teachings for the deaf and hard-of-hearing community. Despite the critical necessity for inclusive religious education, the deaf Muslim community faces a profound technological deficit. There is a severe lack of automated systems capable of accurately translating the unique, linguistically complex, and culturally sensitive gestures of Quranic Sign Language in real-time. By leveraging deep learning, the system uses the YOLO (You Only Look Once) object detection model combined with TensorFlow and OpenCV to detect hand gestures representing Quranic letters. A Quranic Sentence Builder was also developed to convert gesture sequences into meaningful sentences. The system achieved high performance, recording 96.54% precision, 95.72% recall, and a 95.26% F1-score. The findings demonstrate the potential of deep learning in supporting inclusive religious education through accurate and efficient sign language translation.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, YOLO, MySL
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	12 Oct 2026 03:56
Last Modified:	12 Oct 2026 03:56
URII:	http://shdl.mmu.edu.my/id/eprint/16907

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