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

作　　者：Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Ayman Yafoz Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed 

机构地区：[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

出　　处：《Computer Systems Science & Engineering》2024年第5期1387-1403,共17页计算机系统科学与工程（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R263);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia;the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR39.

摘　　要：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 nationswritten 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 optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor 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.

关 键 词：Arabic language handwritten character recognition deep learning CLASSIFICATION parameter tuning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]