Mohammed Maray¹, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Saeed Masoud Alshahrani^4,*, Najm Alotaibi⁵, Sana Alazwari⁶, Mahmoud Othman⁷, Manar Ahmed Hamza⁸

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5467-5482, 2023, DOI:10.32604/cmc.2023.033534

Abstract 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… More >

Bassam A. Y. Alqaralleh^1,*, Fahad Aldhaban¹, Feras Mohammed A-Matarneh², Esam A. AlQaralleh³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3913-3927, 2022, DOI:10.32604/cmc.2022.026531

Abstract In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities. The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages. Automatic lexical task participation might be elaborated to various applications in the NLP. It is apparent from the availability of effective machine recognition models and open access handwritten databases. Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left. Arabic handwritten Character Recognition (HCR) is… More >