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H. Ishida, et al., “A Hilbert Warping Method for HandWriting Gesture Recognition,” Pattern Recogn., Vol. 43, 2010, pp. 2799-2806. doi:10.1016/j.patcog.2010.02.021

has been cited by the following article:

• TITLE: Handwriting Classification Based on Support Vector Machine with Cross Validation

  AUTHORS: Anith Adibah Hasseim, Rubita Sudirman, Puspa Inayat Khalid

  KEYWORDS: Support Vector Machine; Handwriting Difficulties; Cross-Validation

  JOURNAL NAME: Engineering, Vol.5 No.5B, July 29, 2013

  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.