DGCURE: Model for Detection of Dysgraphia

Abstract

Leaming disability is a condition that includes a direct impact on the brain and there's no remedy or any distinguished restorative medicines. Children with learning disability have inconvenience with learning compared to their individual peers and quite regularly fall back academically since a larger part of them go undiscovered. Dysgraphia, which is known as a writing disorder, is a particular disorder of writing with respect to the propagation of in sequential order and numerical signs. Since the causes of dysgraphia are obscure, the early detection of dysgraphia is exceptionally vital. This paper points to analyze children with Dysgraphia, classify them based on type and give them with corresponding treatments. This can be fundamentally done by examining the writing dynamics of children. Deep learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect and extract various properties of handwriting and outputs from the convolutional neural network are fed into the models used for screening the disabilities