Texture feature evaluation for segmentation of historical document images
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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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 new method for off-line writer identification which is based on Farsi handwriting and 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. Also, a new feature extraction method is proposed which is based on Gabor-energy and moments. For the first, we survey different methods for feature extraction from output of Gabor filters. These methods with co-occurrence matrix and Said method are implemented and experimental results on handwriting of 40 peoples demonstrate that the proposed method achieves better performance on Farsi handwritten documents.