Dysgraphia Detection Using Deep Learning

Abstract

Dysgraphia is a neurobiological disorder that impairs an individual's ability to write, often resulting in unclear and irregular handwriting, reduced writing speed, and difficulties with pencil grip and spacing. Early detection and accurate classification of these disabilities are essential for effective intervention and support. Traditional assessment methods, including manual evaluations lack the precision needed for reliable diagnosis, leading to inconsistent identification of learning disabilities. Here, the proposed system is based on deep learning for the detection and classification of learning disabilities through handwriting analysis. Utilizing a dataset of handwritten samples from children, the model employs advanced Convolutional Neural Network (CNN) architectures and Vision Transformers, to accurately identify and classify handwriting patterns indicative of dysgraphia. By leveraging these sophisticated models, this proposed system provides timely and precise diagnoses, enabling tailored educational strategies and interventions for affected children, ultimately enhancing their learning outcomes and quality of life.