Off-line handwritten character recognition by SVM based on the virtual examples synthesized from on-line characters

Abstract

This paper proposes a method to improve the off-line character classifiers which are learned from examples by using the virtual examples synthesized from on-line character database. To obtain the good classifiers, usually a large database which contains enough number of variations of handwritten characters is required. However, in practice collecting enough number of data is time-consuming and costly. In this paper, we propose a method to train SVM for off-line character recognition based on the artificially augmented examples using on-line characters. In our method, a virtual example is synthesized from an on-line character by applying affine transformation to each stroke. SVM classifiers are trained by using the artificially generated patterns. To reject inappropriate artificial examples, a preliminary SVM is learned from the original set of samples, and then used for data selection. Using the augmented training samples, the final SVM is obtained. We examine the effectiveness of the proposed method by experiments of handwritten Japanese Hiragana character classification.