Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

TL;DR

This paper introduces a deep learning approach using CNNs for handwriting-based gender classification, demonstrating superior accuracy over human examiners on a new bilingual dataset of handwritten samples.

Contribution

The paper presents a novel CNN model for gender classification from handwriting and introduces a new bilingual dataset of handwritten samples.

Findings

Deep learning approach outperforms human examiners in accuracy

Proposed CNN effectively extracts features for gender classification

New dataset includes Hebrew and English handwritten samples

Abstract

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans.