Writer’s Gender Classification Using HOG and LBP Features

Abstract

The gender identification in handwritten documents becomes to gain importance for various writer authentication purposes. It provides information for anonymous documents for which we need to know if they were written by a Man or a Woman. In this work, we propose a system for writer’s gender classification that is based on local textural and gradient features. Especially our proposed features are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), which are successful in various pattern recognition applications. The classification step is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset showed that the proposed features provide quite promising results.