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A Hierarchical Handwritten Offline Signature Recognition System

This paper presents an original approach for solving the problem of offline handwritten signature recognition, and a new hierarchical, data-partitioning based solution for the recognition module. Our approach tackles the problem we encountered with an earlier version of our system when we attempted to increase the number of classes in the dataset: as the complexity of the dataset increased, the recognition rate dropped unacceptably for the problem considered. The new approach employs a data partitioning strategy to generate smaller sub-problems, for which the induced classification model should attain better performance. Each sub-problem is then submitted to a learning method, to induce a classification model in a similar fashion with our initial approach. We have performed several experiments and analyzed the behavior of the system by increasing the number of instances, classes and data partitions. We continued using the Naïve Bayes classifier for generating the classification models for each data partition. Overall, the classifier performs in a hierarchical way: a top level for data partitioning via clustering and a bottom level for classification sub-model induction, via the Naïve Bayes classifier. Preliminary results indicate that this is a viable strategy for dealing with signature recognition problems having a large number of persons.