The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification o

This research is intended to high light the uses of political content in foreign Arabic / speaking websites, such as “ CNN “ and” Euro News“, The research problem stems from the main question: What is the nature of the use of the websites in the political content provided through them? A set of sub-questions that give the research aspects and aims to achieve a set of objectives , including the identification of topics that included , the political content provided through , the sample sites during the time period for analysis and determine that the study uses descriptive research based on the discovery of the researcher, describing it accurately and defining the relations between the components.
The research conducted the des

Arabic language is a highly inflectional language where a single word can have different forms using a single root with different interpretations. Arabic does not have a standard way to find roots, the reasons for having inflectional language: suffix, prefix and infix Vowels, which built in complex processes. That is why, words require good processing for information retrieval solutions, until now, and there has been no standard approach to attaining the fully proper root. The applications on Arabic words show around 99% are derived from a combination of bilateral, Trilateral and quad lateral roots.
Processing word- stemming levels in order to extract a root is the process of removing all additional affixes. In case the process of mat

abstract

The grammatical tools (the letters of meanings) are of great importance in understanding the meanings of the Arabic sentences,

This research is a simple attempt to show how our venerable scholars employed the meanings of these tools when they interpreted the linguistic evidence, that is, the grammatical structure largely depends on the tool in forming the meaning within the sentences and employing the meanings of these grammatical tools in explaining the linguistic evidence by clarifying their significance in the contexts of their use and effectiveness. Synthesis of the meanings of grammatical tools is an important tool in understanding the linguistic structure in order to reveal its meaning.

Contrary to deconstruction and its destructive pursuit, the concept of undermining the familiar seeks to refute the constants and its known limitations. It is done through the process of receiving and what is imposed by the formation of the word or text or the structural and design structure in general, along with the Arabic calligraphy in particular. This is based on the recipient's understanding and interpretation of the dual phenomenon and the content's manifestation. More accurately, the disclosure of its reality through its expressive phenomenology; for that sake, the research was devoted to studying “undermining the familiar and embodiment content in Arabic calligraphy” including four chapters. The first chapter comprised

This research examines the phonological adaptation of pure vowels in English loanwords in Iraqi Arabic (IA). Unlike previous small-scale studies, the present study collected 346 loanwords through document review and self-observation, and then analyzed them using quantitative content analysis to identify the patterns of pure vowel adaptation involved in incorporating English loanwords into IA. The content analysis findings showed that most pure vowel adaptations in English loanwords in IA follow systematic patterns and may thus be attributed to specific characteristics of both L1 and L2 phonological systems. Specifically, the findings suggest that the IA output forms typically preserve the features of the input pure vowel to the maxi

The artistic signature of calligraphers has been regarded as a significant aspect of Arabic calligraphy since its inception. As the art form evolved and acquired an aesthetic dimension, the artistic signature became an integral part of this dimension. The calligrapher failed to include his name on the frames, a practice that has become customary among calligraphers nowadays. This tradition allowed to trace the evolution of Arabic calligraphy and identify certain gaps in the calligraphy composition. Additionally, the inclusion of calligrapher's name contributes to the achievement of visual balance within the calligraphy composition, signifying consistency or formal separation. The current study concentrated to investigate the aesthet

    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet