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Signature verification using Kolmogorov-Smirnov statistic

Automatic signature verification of scanned documents are presented here. The strategy used for verification is applicable in scenarios where there are multiple knowns(genuine signature samples) from a writer. First the learning process invovles learning the variation and similarities from the known genuine samples from the given writer and then classification problem answers the question whether or not a given questioned sample belongs to the ensemble of known samples or not. The learning strategy discussed, compares pairs of signature samples from amongst the knwon samples, to obtain a distribution in distance space, that represents the distribution of the variation amongst samples, for that particular writer. The corresponding classification method involves comparing the questioned sample, with all the available knowns, to obtain another distribution in distance space. The classification task is now to compare the two distributions to obtain a probability of similarity of the two distributions, that represents, the probability of the questioned sample belonging to the ensemble of the knowns. The above strategies are applied to the problem of signature verification and performance results are presented.