Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Ayman Yafoz⁴, Mahmoud Othman⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1387-1403, 2024, DOI:10.32604/csse.2023.033902 - 13 September 2024

Abstract 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 nations written 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 optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The… More >

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 - 28 December 2022

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… More >

Nagwa Elaraby^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288 - 22 September 2022

Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two… More >

Yu Xue^1,2,*, Yiling Tong¹, Ziming Yuan¹, Shoubao Su², Adam Slowik³, Sam Toglaw⁴

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 497-509, 2021, DOI:10.32604/iasc.2021.016884 - 16 June 2021

Abstract Because of the characteristics of high redundancy, high parallelism and nonlinearity in the handwritten character recognition model, the convolutional neural networks (CNNs) are becoming the first choice to solve these complex problems. The complexity, the types of characters, the character similarity of the handwritten character dataset, and the choice of optimizers all have a great impact on the network model, resulting in low accuracy, high loss, and other problems. In view of the existence of these problems, an improved LeNet-5 model is proposed. Through increasing its convolutional layers and fully connected layers, higher quality features… More >

Munish Kumar¹, Simpel Rani Jindal²

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.2, pp. 195-202, 2017, DOI:10.3970/cmes.2017.113.201

Abstract Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals. This paper presents a framework for grading of Devanagari writers in perspective of their handwriting. This framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated process. Selecting the set of features is a challenging task for implementing a handwriting grading system of particular language. In this paper, curvature features, namely, parabola curve fitting and power curve fitting have been considered for extracting… More >

Abdurazzag Ali Aburas¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 1-2, 2009, DOI:10.3970/icces.2009.009.001

Abstract The typical Optical Character Recognition (OCR) systems, regardless the character's nature, are based mainly on three stages, preprocessing, features extraction and discrimination (recognizer). Each stage has its own problems and effects on the system efficiency such as time consuming and recognition errors. In order to avoid these difficulties this talk presents new construction of OCR system without pre-processing, features extraction and classifier for any handwriting characters using standard and advanced Image Compression techniques. The proposed algorithms obtained promising results in terms of accuracy as well as in terms of time consuming. More >