Analysis on Handwriting Using Pen-Tablet for Identification of Person and Handedness

Abstract

Human handwriting has some unique properties to express the behaviors and personality of any person. The state-of-the-art systems analyze the handwriting on paper manually and demand human expertness. Moreover, it is very difficult to recognize a person by analyzing the handwriting manually. Thus, this paper proposes a system for person identification along with another system to identify the handedness of individuals using handwriting data analysis. The used handwritten texts for these studies have been collected by the pen-tablet. Six parameters are captured and distinguishable features are extracted to identify the person's handwriting attributes. To identify the person and handedness using extracted features, two different classification algorithms, i.e., Support Vector Machine with linear kernel and Random Forest Classifier, are used. The proposed systems show 69.39% and 72.44 % accuracy using SVM and random forest classifier respectively for person identification and 97.25% accuracy for handedness identification after using SVM.