Deep Learning-based Dysgraphia Detection

This study proposes a CNN-based machine learning framework for early detection of dysgraphia in children aged 8–15, using handwriting data collected via a WACOM tablet. By integrating static and dynamic handwriting features with data augmentation, the model achieved 89% classification accuracy, demonstrating its potential for robust dysgraphia diagnosis.