Abstract
Dysgraphia is a learning disorder that affects handwriting abilities, making it challenging for children to write legibly and consistently. Early detection and monitoring are crucial for providing timely support and interventions. This study applies deep learning techniques to address the dual tasks of dysgraphia detection and optical character recognition (OCR) on handwriting samples from children with potential dysgraphic symptoms. Using a dataset of handwritten samples from Malaysian schoolchildren, we developed a custom Convolutional Neural Network (CNN) model, alongside VGG16 and ResNet50, to classify handwriting as dysgraphic or non-dysgraphic. The custom CNN model outperformed the pre-trained models, achieving a test accuracy of 91.8% with high precision, recall, and AUC, demonstrating its robustness in identifying dysgraphic handwriting features. Additionally, an OCR pipeline was created to segment and recognize individual characters in dysgraphic handwriting, achieving a character recognition accuracy of approximately 43.5%. This research highlights the potential of deep learning in supporting dysgraphia assessment, laying a foundation for tools that could assist educators and clinicians in identifying dysgraphia and tracking handwriting progress over time. The findings contribute to advancements in assistive technologies for learning disabilities, offering hope for more accessible and accurate diagnostic tools in educational and clinical settings.
Abstract (translated)
发育性书写障碍是一种影响书写能力的学习障碍，使得儿童难以清晰且一致地书写。早期检测和监控对于提供及时的支持和干预至关重要。本研究应用深度学习技术来解决识别发育性书写障碍及手写样本光学字符识别（OCR）的双重任务。我们使用了一组来自马来西亚学生的手写样本数据集，开发了一个自定义卷积神经网络（CNN）模型，并与VGG16和ResNet50进行了对比，以对手写体进行分类，判断其是否为发育性书写障碍。自定义CNN模型的表现优于预训练模型，在测试中达到了91.8%的准确率，具有高精确度、召回率和AUC值，展示了其在识别发育性书写特征方面的稳健性。此外，还建立了一个OCR管道来分割并识别发育性书写障碍手写体中的单个字符，实现了大约43.5%的字符识别准确性。本研究强调了深度学习在支持发育性书写障碍评估方面的潜力，为开发有助于教育者和临床医生识别发育性书写障碍及跟踪随时间推移的书写进步情况的工具奠定了基础。该研究成果促进了辅助技术的发展，为学习障碍提供了更加便捷且准确的诊断工具，在教育和临床环境中带来了希望。
URL
https://arxiv.org/abs/2411.13595
