Sardar Hasen Ali^*, Maiwan Bahjat Abdulrazzaq

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356

Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

Nesreen M. Alharbi^*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani

Computer Systems Science and Engineering, Vol.48, No.1, pp. 175-198, 2024, DOI:10.32604/csse.2023.041044

Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… 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

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 >

Nagwa Elaraby^*, Sherif Barakat, Amira Rezk

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

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 CNNs from scratch with randomly… 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

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 can be extracted. Secondly, a… More >

Gibrael Abosamra^*, Hadi Oqaibi

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318

Abstract Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset (EMNIST). The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability. Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset. The presence of intra-class variability is mainly due to different shapes written by different writers for the same character. In this… More >

Sulaiman Khan¹, Shah Nazir¹, Habib Ullah Khan^2,*, Anwar Hussain¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2831-2844, 2021, DOI:10.32604/cmc.2021.015054

Abstract During the last two decades significant work has been reported in the field of cursive language’s recognition especially, in the Arabic, the Urdu and the Persian languages. The unavailability of such work in the Pashto language is because of: the absence of a standard database and of significant research work that ultimately acts as a big barrier for the research community. The slight change in the Pashto characters’ shape is an additional challenge for researchers. This paper presents an efficient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques. These… 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 >

Mamouni El Mamoun^1,*, Zennaki Mahmoud¹, Sadouni Kaddour¹

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 55-66, 2019, DOI:10.32604/cmc.2019.06444

Abstract The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained. 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 the vital information of writers,… More >