Data Driven Feature Extraction for Gender Classification using Multi-script Handwritten Texts

Abstract

This paper presents a study on assessing the effectiveness of machine learned features to predict gender of writers from images of handwriting. Pre-trained Convolutional Neural Networks have been employed as feature extractors to discriminate male and female handwriting while classification is carried out using a number of classifiers, Linear Discriminant Analysis (LDA) being the most effective. Feature extraction is carried out by changing the scale of observation using word, patch and page images. Experiments are carried out on English and Arabic handwriting samples of the QUWI database and the realized results demonstrate the effectiveness of machine learned features in predicting gender from handwriting.