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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
A Hybrid Deep Learning Model for Arabic Text Recognition
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110816
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.
Abstract: Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning (DL) models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using (18) different Arabic font types. The objective of the testing process was to assess the model’s capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data. The prepared model, the custom datasets and the toolkit for generating similar datasets are made publically available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.
Keywords: Arabic optical character recognition; deep learning; convolutional neural networks; recurrent neural networks
Mohammad Fasha, Bassam Hammo, Nadim Obeid and Jabir AlWidian, “A Hybrid Deep Learning Model for Arabic Text Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110816
@article{Fasha2020,
title = {A Hybrid Deep Learning Model for Arabic Text Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110816},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110816},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Mohammad Fasha and Bassam Hammo and Nadim Obeid and Jabir AlWidian}
}
