Deep-learning for dysgraphia detection in children handwritings

Abstract

Early identification of dysgraphia in children is crucial for timely intervention and support. Traditional methods, such as the Brave Handwriting Kinder (BHK) test, which relies on manual scoring of handwritten sentences, are both time-consuming and subjective posing challenges in accurate and efficient diagnosis. In this paper, an approach for dysgraphia detection by leveraging smart pens and deep learning techniques is proposed, automatically extracting visual features from children's handwriting samples. To validate the solution, samples of children handwritings have been gathered and several interviews with domain experts have been conducted. The approach has been compared with an algorithmic version of the BHK test and with several elementary school teachers' interviews.