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arXiv · 2410.19821

Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning

Mahmoud Robaa·Mazen Balat·Rewaa Awaad·Esraa Omar·Salah A. Aly
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Abstract

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.

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Mahmoud Robaa, Mazen Balat, Rewaa Awaad, Esraa Omar, Salah A. Aly. 2024-10-18. Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning. https://arxiv.org/abs/2410.19821

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