Boosting Handwritten Arabic Text Recognition using Deep Autoencoders and Data Augmentation Techniques

Authors

  • Hicham Lamtougui Computer Science Department, Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
  • Hicham El Moubtahij Higher School of Technology, University of Ibn Zohr, Agadir, Morocco
  • Hassan Fouadi Computer Science Department, Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
  • Khalid Satori Computer Science Department, Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Keywords:

handwritten characters, Deep Learning, data augmentation, Variational AE

Abstract

The recognition of handwritten characters and numbers is a complex challenge in the field of pattern recognition, especially for the Arabic language. While significant progress has been made for the automatic recognition of Latin handwritten characters, methods and approaches for the Arabic language remain insufficient. Deep learning technologies, in particular auto-encoders (AE), offer new perspectives for handwriting recognition. In this article, we introduce the different types of most popular AE, such as Convolutional AE (CAE), Sparse AE (SAE), Denoising AE (DAE), and Variational AE (VAE), and evaluate their performance on two reference databases: the Modified Arabic Digits dataBase (MADBase) and Arabic Handwritten Character Dataset (AHCD). Using data augmentation to improve results, the VAE algorithm showed higher accuracy than other Deep Learning algorithms on both databases, with very encouraging results of 98.77% for MADBase and 98.42% for AHCD.

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Published

21.09.2023

How to Cite

Lamtougui, H. ., Moubtahij, H. E. ., Fouadi, H. ., & Satori, K. . (2023). Boosting Handwritten Arabic Text Recognition using Deep Autoencoders and Data Augmentation Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 800–809. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3613

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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