Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning
It addresses early detection and trust-building for dyslexia diagnosis globally, though it is incremental as it builds on existing transfer learning and transformer methods.
This study tackled the problem of detecting dyslexia through handwriting analysis by introducing an explainable AI framework, achieving a test precision of 99.65% and surpassing state-of-the-art methods.
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.