Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts

Mohamed Eltay, Abdelmalek Zidouri*, Irfan Ahmad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Recognition of cursive handwritten Arabic text is a difficult problem because of context-sensitive character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. In this paper, we review and investigate different deep learning architectures and modeling choices for Arabic handwriting recognition. Further, we address the problem that imbalanced data sets present to deep learning systems. In order to address this issue, we are presenting a novel adaptive data-augmentation algorithm to promote class diversity. This algorithm assigns a weight to each word in the database lexicon. This weight is calculated based on the average probability of each class in a word. Experimental results on the IFN/ENIT and AHDB databases have shown that our presented approach yields state-of-the-art results.

Original languageEnglish
Article number9091836
Pages (from-to)89882-89898
Number of pages17
JournalIEEE Access
StatePublished - 2020

Bibliographical note

Funding Information:
This work was supported by the King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.

Publisher Copyright:
© 2013 IEEE.


  • Arabic handwriting recognition (AHR)
  • Connectionist temporal classification (CTC)
  • Convolutional neural networks (CNN)
  • Deep learning neural network (DLNN)
  • IFN/ENIT database
  • Long short-term memory (LSTM)
  • Recurrent neural network (RNN)

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)
  • Electrical and Electronic Engineering


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