Arabic Handwriting Classification using Deep Transfer Learning Techniques

Ali Abd Almisreb, Nooritawati Md Tahir, Sherzod Turaev, Mohammed A. Saleh, Syed Abdul Mutalib Al Junid

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

Original languageEnglish
Pages (from-to)641-654
Number of pages14
JournalPertanika Journal of Science and Technology
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 1 2022

Keywords

  • Arabic text recognition
  • Deep learning
  • Handwriting classification
  • Transfer learning

ASJC Scopus subject areas

  • General Computer Science
  • General Chemical Engineering
  • General Environmental Science
  • General Agricultural and Biological Sciences

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