Extraction of Parameters From Dysgraphic Handwriting for CDSS Systems

Abstract

In this study we address the issue of the handwriting processing by extracting parameters from the written speech. The work applies machine learning method-the decision trees method which aims to recognize the impaired handwriting, particularly dysgraphia. 55 features (e.g. total time, pen movement, pressure, speed, acceleration) were extracted from each out of 80 handwriting samples while analyzing the performance of classifier for the dominant parameter-minimal speed, and without the dominant parameter as well. The experimental results of the classifier are compared to the results of the statistical test-Mann Whitney U-test as a complex and challenging endeavor to create an accurate classification.