This study introduces an explainable AI (XAI) framework for the detection of dyslexia through handwriting analysis, achieving an impressive test precision of 99.65%. The framework integrates transfer learning and transformer-based models, identifying handwriting features associated with dyslexia while ensuring transparency in decision-making via Grad-CAM visualizations. Its adaptability to different languages and writing systems underscores its potential for global applicability. By surpassing the classification accuracy of state-of-the-art methods, this approach demonstrates the reliability of handwriting analysis as a diagnostic tool. The findings emphasize the framework's ability to support early detection, build stakeholder trust, and enable personalized educational strategies.
@article{arxiv.2410.19821,
title = {Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning},
author = {Mahmoud Robaa and Mazen Balat and Rewaa Awaad and Esraa Omar and Salah A. Aly},
journal= {arXiv preprint arXiv:2410.19821},
year = {2024}
}