Dysgraphia Identification from Handwriting with Support Vector Machine Method

Abstract

Abstract Dysgraphia, a handwriting disorder in which a person has difficulty in writing at any level such as slow writing or unreadable letter. Many research has done to study the characteristics and to diagnose it for early prevention in children. In this study, we try to identify dysgraphia among children and divide it into 4 class, normal, light, moderate, and severe. Therefore an android application with embedding a handwriting recognition tool was created to collect the data from elementary school students that have dysgraphia and those who don’t. We use Support Vector Machine in classifying the data to identify dysgraphia because SVM has the ability to learn well with limited data compared to ANN on many occasions. The result, after using three different kernels in SVM such as Linear, Polynomial, and Radial Base Function kernel (RBF), shows that the RBF kernel produces better average accuracy and Cohen’s kappa value compared to Linear and Polynomial kernels, where the average accuracy of each kernel is 78.56% for Linear, 81.40% for Polynomial, and 82.51% for RBF.