Virtual Example Synthesis Based on PCA for Off-Line Handwritten Character Recognition

Abstract

This paper proposes a method to improve off-line character classifiers learned from examples using virtual examples synthesized from an on-line character database. To obtain good classifiers, a large database which contains a large enough number of variations of handwritten characters is usually required. However, in practice, collecting enough data is time-consuming and costly. In this paper, we propose a method to train SVM for off-line character recognition based on artificially augmented examples using on-line characters. In our method, virtual examples are synthesized from on-line characters by the following two steps: (1) applying affine transformation to each stroke of “real” characters, and (2) applying affine transformation to each stroke of artificial characters, which are synthesized on the basis of PCA. SVM classifiers are trained by using the training samples containing artificially generated patterns and real characters. We examine the effectiveness of the proposed method with respect to the recognition rates and number of support vectors of SVM through experiments involving the handwritten Japanese Hiragana character classification.