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A method for Off-line handwritten signature verification is described in this paper. Recently, several papers have proposed pseudo dynamic methods based on the ink deposition process to discriminate between genuine and fake signatures. The major problem of those methods is the ink texture normalization in order to make the system invariable to the pen. The more extreme pen normalization is the binarization. This paper explores the usefulness of texture based measures with binarized signatures. In particularly, it proposes to apply the gray scale features local binary pattern (LBP) and local directional pattern (LDP) features to characterize black and white static signatures. The experiments done with MCYT75, GPDS300signature and GPDS960signature corpus shown that LDP are very adequate parameters for automatic verification of black and white static signatures. The results are obtained training a Support Vector Machine (SVM) classifier with genuine samples and random forgeries while random and skilled forgeries have been used for testing it.