TY  - EJOU
AU  - Maray, Mohammed 
AU  - Al-onazi, Badriyya B. 
AU  - Alzahrani, Jaber S. 
AU  - Alshahrani, Saeed Masoud 
AU  - Alotaibi, Najm 
AU  - Alazwari, Sana 
AU  - Othman, Mahmoud 
AU  - Hamza, Manar Ahmed 

TI  - Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 74
IS  - 3
SN  - 1546-2226

AB  - The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability 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 with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce 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 the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.
KW  - Arabic language; handwritten character recognition; deep learning; feature extraction; hyperparameter tuning

DO  - 10.32604/cmc.2023.033534