Salah Alghyaline^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4905-4924, 2024, DOI:10.32604/cmc.2024.052016 - 20 June 2024

Abstract Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three More >

Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426 - 29 April 2023

Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full… More >

Salah Alghyaline^*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1825-1861, 2023, DOI:10.32604/cmes.2022.024555 - 23 November 2022

Abstract This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher’s contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance… More >

Sunil Dhankhar^1,*, Mukesh Kumar Gupta¹, Fida Hussain Memon^2,3, Surbhi Bhatia⁴, Pankaj Dadheech¹, Arwa Mashat⁵

Computer Systems Science and Engineering, Vol.43, No.1, pp. 397-412, 2022, DOI:10.32604/csse.2022.024059 - 23 March 2022

Abstract In today’s digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position,… More >

Samah Ibrahim Alshathri^1,*, Desiree Juby Vincent², V. S. Hari²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1371-1386, 2022, DOI:10.32604/cmc.2022.022458 - 03 November 2021

Abstract Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for… More >