Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition  

作　　者：Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza 

机构地区：[1]Department of Information Systems,College of Computer Science,King Khalid University,Abha,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,Saudi Arabia [4]Department of Computer Science,College of Computing and Information Technology,Shaqra University,Shaqra,Saudi Arabia [5]Prince Saud AlFaisal Institute for Diplomatic Studies,Riyadh,Saudi Arabia [6]Department of Information Technology,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia [7]Department of Computer Science,Faculty of Computers and Information Technology,Future University in Egypt,New Cairo,11835,Egypt [8]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,AlKharj,Saudi Arabia

出　　处：《Computers, Materials & Continua》2023年第3期5467-5482,共16页计算机、材料和连续体（英文）

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(168/43);Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia;The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR32);The author would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work。

摘　　要：The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关 键 词：Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]