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Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Abdelwahed Motwakel1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Ayman Yafoz4, Mahmoud Othman5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Amgad Atta Abdelmageed1

1 Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, 16278, Saudi Arabia
2 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Makkah, 24211, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Abdelwahed Motwakel. Email: email

Computer Systems Science and Engineering 2024, 48(5), 1387-1403. https://doi.org/10.32604/csse.2023.033902

Abstract

Handwritten character recognition becomes one of the challenging research matters. More studies were presented for recognizing letters of various languages. The availability of Arabic handwritten characters databases was confined. Almost a quarter of a billion people worldwide write and speak Arabic. More historical books and files indicate a vital data set for many Arab nations written in Arabic. Recently, Arabic handwritten character recognition (AHCR) has grabbed the attention and has become a difficult topic for pattern recognition and computer vision (CV). Therefore, this study develops fireworks optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The major intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language. It initially pre-processes the handwritten images to improve their quality of them. Then, the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors. Next, the deep echo state network (DESN) model is utilized to classify handwritten characters. Finally, the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance. Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique. The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches, with 99.91% and 98.94% on Hijja and AHCD datasets, respectively.

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APA Style
Motwakel, A., Al-onazi, B.B., Alzahrani, J.S., Yafoz, A., Othman, M. et al. (2024). Fireworks optimization with deep learning-based arabic handwritten characters recognition model. Computer Systems Science and Engineering, 48(5), 1387-1403. https://doi.org/10.32604/csse.2023.033902
Vancouver Style
Motwakel A, Al-onazi BB, Alzahrani JS, Yafoz A, Othman M, Zamani AS, et al. Fireworks optimization with deep learning-based arabic handwritten characters recognition model. Comput Syst Sci Eng. 2024;48(5):1387-1403 https://doi.org/10.32604/csse.2023.033902
IEEE Style
A. Motwakel et al., "Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model," Comput. Syst. Sci. Eng., vol. 48, no. 5, pp. 1387-1403. 2024. https://doi.org/10.32604/csse.2023.033902



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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