# Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning

arXiv: https://arxiv.org/abs/2410.19821 · HTML: https://mlsanity.com/paper/2410.19821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Handwriting Anomalies and Learning Disabilities through Recurrent Neural Networks and Geometric Pattern Analysis](https://mlsanity.com/paper/2405.07238/) (2024)
- [Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach](https://mlsanity.com/paper/2406.00409/) (2024)

## Abstract (arXiv)

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.