Improving writer identification by means of feature selection and extraction

Abstract

To identify the author of a sample handwriting from a set of writers, 100 features are extracted from the handwriting sample. By applying feature selection and extraction methods on this set of features, subsets of lower dimensionality are obtained. We show that we can achieve significantly better writer identification rates if we use smaller feature subsets returned by different feature extraction and selection methods. The methods considered in this paper are feature set search algorithms, genetic algorithms, principal component analysis, and multiple discriminant analysis.