Text independent writer identification based on Gabor filter and SVM classifier

Abstract

Writer identification has become a hot topic in pattern recognition and machine learning research area. This paper studies on the technology of text independent writer identification based on texture analysis. At first in the preprocessing stage the uniform texture images are created from the input document. An approach for improved characters segmentation is presented based on analysis for the character elements and their topological relations. Then the 32-channel Gabor filter is utilized to extract 64 texture features of writing image by calculating the mean values and the standard deviations of filtering output images. Finally, multi-class support vector machines (SVM) classifier is adopted to fulfill the identification task. The experiment result shows that the scheme is effective and promising.