LLM-Enhanced Braille-To-Speech Learning Assistant for Visually Impaired Users Using AraT5
Document Type
Conference Proceeding
Source of Publication
2026 17th Student Research Conference on Applied Computing Src 2026
Publication Date
4-22-2026
Abstract
Braille literacy remains essential for visually impaired individuals to access written information and achieve independence in education and daily communication. However, learning Braille can be challenging due to the lack of immediate feedback when interpreting Braille dot patterns. Traditional assistive technologies often focus only on recognizing Braille characters and converting them into speech without providing contextual explanations that support learning.This paper proposes an intelligent Braille-To-Speech learning assistant that integrates rule-based Braille decoding with the Arabic transformer language model AraT5. The system converts Braille symbols into text, generates contextual explanations using a large language model, and provides auditory feedback through text-To-speech synthesis.Experiments conducted using a Kaggle Braille dataset demonstrate strong performance, achieving 93% Braille decoding accuracy, 0.84 token-level F1 score, ROUGE-1 score of 0.79, ROUGE-L score of 0.76, and BLEU score of 0.71. These results indicate that combining symbolic Braille decoding with large language models significantly improves accessibility and enhances interactive Braille learning for visually impaired users.
DOI Link
ISBN
[9798319510167]
Publisher
IEEE
Disciplines
Computer Sciences
Keywords
Accessibility Systems, AraT5, Assistive Technologies, Braille Recognition, Deep Learning, Large Language Models, Text-To-Speech, Visual Impairment Support
Scopus ID
Recommended Citation
Alahmed, Alya; Saleh, Al Anood; Belqasmi, Fatna; and Alkhatib, Manar, "LLM-Enhanced Braille-To-Speech Learning Assistant for Visually Impaired Users Using AraT5" (2026). All Works. 8217.
https://zuscholars.zu.ac.ae/works/8217
Indexed in Scopus
yes
Open Access
no