Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition

Mohamed Eltay, Abdelmalek Zidouri*, Irfan Ahmad, Yousef Elarian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Training deep learning based handwritten text recognition systems needs a lot of data in terms of text images and their corresponding annotations. One way to deal with this issue is to use data augmentation techniques to increase the amount of training data. Generative Adversarial Networks (GANs) based data augmentation techniques are popular in literature especially in tasks related to images. However, specific challenges need to be addressed in order to effectively use GANs for data augmentation in the domain of text recognition. Text data is inherently imbalanced in terms of frequency of different characters appearing in training samples and the training data as a whole. GANs trained on the imbalanced dataset leads to augmented data that does not represent the minority characters well. In this paper, we present an adaptive data augmentation technique using GANs that deals with the issue of class imbalance arising in text recognition problems. We show, using experimental evaluations on two publicly available datasets for handwritten Arabic text recognition, that the GANs trained using the presented technique is effective in dealing with class imbalanced problem by generating augmented data that is balanced in terms of character frequencies. The resulting text recognition systems trained on the balanced augmented data improves the text recognition accuracy as compared to the systems trained using standard techniques.

Original languageEnglish
Article numbere861
JournalPeerJ Computer Science
StatePublished - 2022

Bibliographical note

Funding Information:
This work is supported by King Fahd University of Petroleum & Minerals. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Eltay et al.


  • Adaptive data augmentation
  • Arabic handwriting recognition
  • Convolutional neural networks
  • Deep learning neural networks
  • Generative adversarial networks
  • Handwritten text generation

ASJC Scopus subject areas

  • Computer Science (all)


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