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

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.