@Article{csse.2023.033902,
AUTHOR = {Abdelwahed Motwakel, Badriyya B. Al-onazi, Jaber S. Alzahrani, Ayman Yafoz, Mahmoud Othman, Abu Sarwar Zamani, Ishfaq Yaseen, Amgad Atta Abdelmageed},
TITLE = {Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {48},
YEAR = {2024},
NUMBER = {5},
PAGES = {1387--1403},
URL = {http://www.techscience.com/csse/v48n5/57937},
ISSN = {},
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.},
DOI = {10.32604/csse.2023.033902}
}