Method for Dysgraphia Disorder Detection using Convolutional Neural Network

Abstract

This paper describes a method for dysgraphia disorder detection based on the classification of handwritten text. In the experiment we have verified proposed approach based on the conventional signal theory. Input data consists of the handwritten text by dysgraphia diagnosed children. Techniques for early dysgraphia detection could be applied in the schools to detect children with a possible diagnosis of dysgraphia and early intervention could improve their lives. The main goal of research is to develop a tool based on a machine learning for schools to diagnose dyslexia and dysgraphia. An experiment was performed on the dataset of 120 children in the school age (63 normally developing and 57 dysgraphia diagnosed). The main advantage is the simple algorithm for preprocessing of the raw data. Then was designed simple 3-layers convolutional neural network for classification of data. On the test data, our model reached accuracy 79.7%.

Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art

“…As shown in Table 3, the performance of these classification tools is below that of the paper-and-pen tools listed above (73% for [71]; 79.7% for [72]). The only exception is TestGraphia, the algorithm developed by Dimauro et al [70], with good performances very close to that of the original BHK test.…”

Expert system for diagnosing learning disorders in children

“…Likewise, the study carried out by Skunda et al [15] presents a method for detecting dysgraphia disorders through the classification of handwritten text. The primary goal of the study is to develop a machine learning-based tool that enables schools to diagnose both dyslexia and dysgraphia, with the aim of applying early detection techniques to improve intervention for affected children.…”