Writer Identification Based on Farsi Handwritten Text Using Texture Analysis

Writer Identification Based on Farsi Handwritten Text Using Texture Analysis

Abstract

Writer identification recently has been studied and it has a wide variety of applications. Most studies about writer identification are based on English documents and to our knowledge no research has been reported on Farsi or Arabic documents. In this paper, we have proposed a method for off-line writer identification which is text-independent. Based on the idea that has been presented in the previous studies, here we assume handwriting as texture image and a set of features which are based on multi-channel Gabor filters are extracted from preprocessed image of documents. Substantially, the property of proposed method is using of the bank of Gabor filters which is appropriate for structure of Farsi handwritten texts and vision system. Our method with two methods which are based on co-occurrence matrix and Gabor filters, are implemented and experimental results on handwriting of 25 peoples demonstrate that the proposed method achieves better performance on Farsi handwritten documents.

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