Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah

This research project focuses on Arabic Handwritten Recognition system using Convolutional Neural Networks (CNNs) algorithm. This study delves into the challenging realm of Arabic handwriting recognition, spurred by the intricate nature of the script and the scarcity of specialized tools and high-qu...

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Main Author:	Abdullah, Nurul Amira
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96466/1/96466.pdf
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id	my-uitm-ir.96466
record_format	uketd_dc
spelling	my-uitm-ir.964662024-06-06T03:34:38Z Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah 2024 Abdullah, Nurul Amira Neural networks (Computer science) This research project focuses on Arabic Handwritten Recognition system using Convolutional Neural Networks (CNNs) algorithm. This study delves into the challenging realm of Arabic handwriting recognition, spurred by the intricate nature of the script and the scarcity of specialized tools and high-quality training data. The investigation primarily focuses on the effectiveness of Convolutional Neural Networks (CNNs) in mitigating these challenges through the development of a Handwritten Character Recognition System (HCR) tailored for Arabic script. Leveraging CNNs, the system endeavors to accurately transcribe and comprehend handwritten Arabic documents, thereby facilitating efficient processing and analysis. Through a comprehensive literature review, the research underscores the significance of Arabic handwriting recognition across various domains, such as document digitization, archival systems, historical document analysis, and language learning, particularly among toddlers. Methodologically, the study adopts a structured seven-phase approach, commencing with a preliminary study encompassing a comprehensive literature review to identify the project's objectives, scope, and significance. Subsequent phases include requirement analysis, data collection, prototype design, implementation, evaluation, and documentation. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96466/ https://ir.uitm.edu.my/id/eprint/96466/1/96466.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Halim, Zulkifli
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Halim, Zulkifli
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Abdullah, Nurul Amira Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
description	This research project focuses on Arabic Handwritten Recognition system using Convolutional Neural Networks (CNNs) algorithm. This study delves into the challenging realm of Arabic handwriting recognition, spurred by the intricate nature of the script and the scarcity of specialized tools and high-quality training data. The investigation primarily focuses on the effectiveness of Convolutional Neural Networks (CNNs) in mitigating these challenges through the development of a Handwritten Character Recognition System (HCR) tailored for Arabic script. Leveraging CNNs, the system endeavors to accurately transcribe and comprehend handwritten Arabic documents, thereby facilitating efficient processing and analysis. Through a comprehensive literature review, the research underscores the significance of Arabic handwriting recognition across various domains, such as document digitization, archival systems, historical document analysis, and language learning, particularly among toddlers. Methodologically, the study adopts a structured seven-phase approach, commencing with a preliminary study encompassing a comprehensive literature review to identify the project's objectives, scope, and significance. Subsequent phases include requirement analysis, data collection, prototype design, implementation, evaluation, and documentation.
format	Thesis
qualification_level	Bachelor degree
author	Abdullah, Nurul Amira
author_facet	Abdullah, Nurul Amira
author_sort	Abdullah, Nurul Amira
title	Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
title_short	Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
title_full	Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
title_fullStr	Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
title_full_unstemmed	Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah
title_sort	arabic character recognition system using convolutional neural network (cnn) / nurul amira abdullah
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96466/1/96466.pdf
_version_	1804889989801050112

Arabic character recognition system using Convolutional Neural Network (CNN) / Nurul Amira Abdullah

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