Automatic Gender Classification from Handwritten Images: A Case Study

Irina Rabaev, Marina Litvak, Sean Asulin, Or Haim Tabibi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Using a handwritten sample to automatically classify the writer’s gender is an essential task in a wide range of areas, e.g., psychology, historical documents classification, and forensic analysis. The challenge of gender prediction from offline handwriting can be demonstrated by the relatively low (below 90%) performance of state-of-the-art systems. Despite a high interest within a broad spectrum of research communities, the published works in this area generally concentrate on English and Arabic languages. Most of the existing approaches focus on manual feature selection. In this work, we study an application of deep neural networks for gender classification, where we investigate cross-domain transfer learning with ImageNet pre-training. The study was performed on two datasets, the QUWI dataset, consisting of handwritten documents in English and Arabic, and a new dataset of documents in Hebrew script. We perform extensive experiments, analyze and compare the results obtained with different neural networks. We demonstrate that advanced deep-learning models outperform conventional machine learning approaches that were used in previous studies. We also compare the obtained results against human-level performance and show that the problem is challenging for non-experts.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings
EditorsNicolas Tsapatsoulis, Andreas Panayides, Theo Theocharides, Andreas Lanitis, Andreas Lanitis, Constantinos Pattichis, Constantinos Pattichis, Mario Vento
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030891305
StatePublished - 1 Jan 2021
Externally publishedYes
Event19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021 - Virtual, Online
Duration: 28 Sep 202130 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13053 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021
CityVirtual, Online


  • Deep neural network
  • Gender classification
  • Offline handwriting analysis
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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