2019
DOI: 10.15546/aeei-2019-0007
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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 we… Show more

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Cited by 3 publications
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