Comparison of ANN and SVM to Identify Children Handwriting Difficulties

Anith Adibah Hasseim, Rubita Sudirman, Puspa Inayat Khalid, Narges Tabatabaey-Mashadi

Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia.

DOI: 10.4236/eng.2013.55B001                

Abstract

This paper compares two classification methods to determine pupils who have difficulties in writing. Classification experiments are made with neural network and support vector machine method separately. The samples are divided into two groups of writers, below average printers (test group) and above average printers (control group) are applied. The aim of this paper is to demonstrate that neural network and support vector machine can be successfully used in classifying pupils with or without handwriting difficulties. Our results showed that support vector machine classifier yield slightly better percentage than neural network classifier and it has a much stable result.

Keywords

Neural Network; Support Vector Machine; Handwriting Difficulties

Conflicts of Interest

The authors declare no conflicts of interest.

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