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

arXiv: https://arxiv.org/abs/1912.01816 · HTML: https://mlsanity.com/paper/1912.01816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network](https://mlsanity.com/paper/2409.02584/) (2024)

## Abstract (arXiv)

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.