Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks
This work addresses gender classification from handwriting, a domain-specific problem, with an incremental improvement using deep learning over traditional methods.
The paper tackled handwriting-based gender classification by proposing a convolutional neural network for automatic feature extraction and classification, achieving substantially higher accuracy than human examiners on a new dataset of 405 participants.
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.