Improving Writer Identification Through Writer Selection

In this work we present a method for selecting instances for a writer identification system underpinned on the dissimilarity representation and a holistic representation based on texture. The proposed method is based on a genetic algorithm that surpasses the limitations imposed by large training sets by selecting writers instead of instances. To show the efficiency of the proposed method, we have performed experiments on three different databases (BFL, IAM, and Firemaker) where we can observe not only a reduction of about 50% in the number of writers necessary to build the dissimilarity model but also a gain in terms of identification rate. Comparing the writer selection with the traditional instance selection, we could observe that both strategies produce similar results but the former converges about three times faster.