Handwriting Classification Based on Support Vector Machine with Cross Validation

Anith Adibah Hasseim,Puspa Inayat Khalid,Rubita Sudirman

doi:10.4236/eng.2013.55b017

Abstract

Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method for training and validating is introduced. The aim of this paper is to compare the performance of support vector machine with RBF and polynomial kernel used for classifying pupils with or without handwriting difficulties. Experimental results showed that the performance of SVM with RBF kernel is better than the one with polynomial kernel.

Highlights

The field of handwriting has been of interest from a variety of aspects; its entity, indications and aesthetic
The aim of this paper is to compare the performance of support vector machine with radial basis function (RBF) and polynomial kernel used for classifying pupils with or without handwriting difficulties
The results reported here have shown that the performance of Support vector machine (SVM) with RBF kernel is better than SVM with polynomial kernel

Summary

Introduction

The field of handwriting has been of interest from a variety of aspects; its entity, indications and aesthetic. The development of handwriting and the factors that affect handwriting performance were investigated [1,2], but later whole words were addressed. Most of the systems reported in the literature until today involved screening measures in identifying pupils who are at risk of handwriting difficulties and addressed the absence of an appropriate tool for monitoring beginning handwriting development. Automated handwriting analysis has been given more attention in the hunt for quantitative features and key indicators in monitoring beginning handwriting skill development. Such automated handwriting analysis include recognizing the writer [3]), the text written [5,6]), or even semantic content of the text More or less each of these issues can, and have been investigated either offline or online related to the available data

Objectives

Methods

Results

Conclusion