This paper proposes an efficient method based on texture feature for text-independent writer identification. In order to extract texture feature, we use the modified 2-D Gabor filter, which can decompose the image into sub-bands with different frequencies and orientations. Nearest neighbor classifier based on weighted chi-square distance is utilized in classification. The experiments on a database containing 203 writers of address images demonstrate that the performance of our modified 2-D Gabor filter is better than that of the traditional 2-D Gabor filter and our proposed method achieves promising results. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Wang, D., Wen, Y., & Lu, Y. (2012). Text-independent writer identification using texture feature. In Communications in Computer and Information Science (Vol. 331 CCI, pp. 162–168). https://doi.org/10.1007/978-3-642-34595-1_23
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