Improving handwriting based gender classification using ensemble classifiers

A system to predict gender from images of handwriting using textural descriptors.Multiple classifiers to discriminate male and female writings.Classifiers combined using bagging, voting and stacking techniques.Generic and script-independent approach applied to English and Arabic handwritings.Improved results on the QUWI database once compared to state-of-the-art methods. This paper presents a system to predict gender of individuals from offline handwriting samples. The technique relies on extracting a set of textural features from handwriting samples of male and female writers and training multiple classifiers to learn to discriminate between the two gender classes. The features include local binary patterns (LBP), histogram of oriented gradients (HOG), statistics computed from gray-level co-occurrence matrices (GLCM) and features extracted through segmentation-based fractal texture analysis (SFTA). For classification, we employ artificial neural networks (ANN), support vector machine (SVM), nearest neighbor classifier (NN), decision trees (DT) and random forests (RF). Classifiers are then combined using bagging, voting and stacking techniques to enhance the overall system performance. The realized classification rates are significantly better than those of the state-of-the-art systems on this problem validating the ideas put forward in this study.