Open Access

ARTICLE

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

Abdelwahed Motwakel^1,*, 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:

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

Received 01 July 2022; Accepted 07 November 2022; Issue published 13 September 2024

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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Keywords

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