Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910750525620224 |
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| author | Robaa, Mahmoud Balat, Mazen Awaad, Rewaa Omar, Esraa Aly, Salah A. |
| author_facet | Robaa, Mahmoud Balat, Mazen Awaad, Rewaa Omar, Esraa Aly, Salah A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning Robaa, Mahmoud Balat, Mazen Awaad, Rewaa Omar, Esraa Aly, Salah A. Computer Vision and Pattern Recognition 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. |
| title | Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19821 |