Combination of topological and local shape features for writer’s gender, Handedness and age classification

Abstract

In this work, writer’s gender, handedness and age range prediction is addressed through automatic analysis of handwritten sentences. Three SVM-based predictors associated to different data features are developed. Then, a Fuzzy MIN-MAX combination rule is proposed to aggregate robust prediction from individual systems. Experiments are carried on two public Arabic and English datasets. Results in terms of prediction accuracy demonstrate the usefulness of the proposed algorithm, which provides a gain between 1% and 10% over both individual systems and classical combination rules. Moreover, it is much more relevant than various state of the art methods.