Hybrid parameterization system for writer identification

Abstract

In this paper, we present a hybrid parameterization system from classical and graphologist features, as the existing percentage of cohesion in the writing of each individual, as well as the smaller and greater axes of the ovals and loops. They have been used on the writer identification together with other parameters applied to handwritten words. That set of characteristics has been tested with our off-line database, which consists of 70 writers with 10 samples per writer and as well each sample is composed of 34 words. We have got a success rate of 96%, applying as classifier Neural Network, and after, the technique of "more voted" algorithm, with 10 Neural Networks.