Published in Scopus

2026-02-09

Dr. Mustafa Hamed Saleh, a lecturer at the Renewable Energy Research Center, University of Anbar, published a research paper with researchers from British universities at the international conference:

"18th International Conference on Development in eSystem Engineering (DeSE)"

titled:

"Machine Learning-Based Enhancements for Indian Sign Language Translation"

This paper presents a sign language translation system developed to enhance accessibility for lesser-supported sign languages, with a specific focus on Indian Sign Language. Given that India has the world's largest deaf population, the communication gap between sign language users and non-users poses a significant barrier. The objective of this research is to examine existing support systems for Indian Sign Language and to build a system capable of detecting, recognizing, and translating signed words through the use of connected cameras. To achieve this goal, image classification using transfer learning techniques was applied to a small dataset of Indian Sign Language gestures, resulting in an average recognition accuracy rate of 83%. Over 10,000 images across 12 different gestures were collected, and the training/validation process reached peak testing accuracy levels of 95%. The final model was deployed into a web-based application specifically designed for use in areas with limited access to advanced technology, aiming to reduce communication barriers and improve inclusivity for the deaf and hard-of-hearing community.