Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

Classification of gender from images of handwriting is an interesting research problem in computerized analysis of handwriting. The correlation between handwriting and gender of writer can be exploited to develop intelligent systems to facilitate forensic experts, document examiners, paleographers, psychologists and neurologists. We propose a handwriting based gender recognition system that exploits texture as the discriminative attribute between male and female handwriting. The textural information in handwriting is captured using combinations of different configurations of oriented Basic Image Features (oBIFs). oBIFs histograms and oBIFs columns histograms extracted from writing samples of male and female handwriting are used to train a Support Vector Machine classifier (SVM). The system is evaluated on three subsets of the QUWI database of Arabic and English writing samples using the experimental protocols of the ICDAR 2013, ICDAR 2015 and ICFHR 2016 gender classification competitions reporting classification rates of 71%, 76% and 68% respectively; outperforming the participating systems of these competitions. While textural measures like local binary patterns, histogram of oriented gradients and Gabor filters etc. have remained a popular choice for many expert systems targeting recognition problems, the present study demonstrates the effectiveness of relatively less investigated oBIFs as a robust textual descriptor. (C) 2018 Elsevier Ltd. All rights reserved.