Dysgraphia Handwriting Image Augmentation for CNN Model Classification

Dysgraphia affects a person’s ability to write consistently and properly especially among school children. It is a challenging condition as it needs effective intervention to help the affected children succeed academically and socially. With the advancement in technology in artificial intelligence (AI), various methods and approaches have been developed using convolutional neural networks (CNN) model to overcome several limitations to assess dysgraphia symptoms. However, there are major concerns about the difficulties of getting large data of dysgraphia handwriting images for CNN attributes model. Thus, this study is aimed to develop dysgraphia handwriting recognition model based on augmentation method. In this study, image augmentation is addressed by creating new data by using rotation and brightness technique to generate a set of synthetic images. The augmented data is trained and tested using CNN classification model to classify four classes of dysgraphia handwriting. The results show a significant improvement with $77 \%$ accuracy using augmented as compared to without augmented data only $\mathbf{7 3 \%}$. This study indicated that augmentation method is significant for inclusion in CNN classification model particularly for dysgraphia potential risk recognition. This study is further recommended to implement intelligence-based augmentation method which can be incorporated into a computer-assisted dysgraphia screening system to provide a rapid, accurate, and unbiased dysgraphia detection.