Review on Dysgraphia Prediction using Handwriting Recognition

In the following paper, we present an overview of current work on handwriting recognition-based dysgraphia prediction. Our analysis reveals both promising insights and limitations. We suggest that to improve the effectiveness of proposed approaches, it is important to compare them with other state-of-the-art methods and expand datasets to include more diverse samples and inclined text lines. We also emphasize the need to consider existing challenges such as sensitivity to image quality and noise, and the importance of replicating studies with larger and more diverse sample sizes. Our findings provide valuable insights for researchers working in this field to guide future developments and improve the accuracy and applicability of handwriting recognition and analysis.