Deep Learning Techniques to Detect Learning Disabilities Among children using Handwriting

Abstract

In today’s world, we come across children facing certain disabilities which pose as obstacles and hinder their academic growth. Some of these disabilities are explicitly visible to the common eye, whereas some are hard to find and need extra attention. One such condition is Motor Dysgraphia which challenges an individual’s ability to write. The common practice that is followed to identify such a condition among children is quite expensive and creates a mental strain on them. There are many intelligent computational methods that have been proposed with bearing a wide range of performances, however they are not quite standardized for assessment. Fortunately the advancements in Deep Learning techniques have been proven beneficial in automating this identification task. In this study, Learning Disability Detection system is built using Deep Learning techniques. The project’s application is mainly focused on the pre-school and primary school children. This model analyses the child’s handwriting and classifies whether the child is subjected to such a disorder or not. Deep Learning models - Convolutional Neural Networks (CNN) and Vision Transformers are adapted and their Disability Detection performances are analyzed and compared.