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

2019-12-04 06:24:31
Evyatar Illouz, Eli David, Nathan S. Netanyahu
arXiv_CV
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/1912.01816

PDF

https://arxiv.org/pdf/1912.01816.pdf

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