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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| Format: | Thesis |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/96466/1/96466.pdf |
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| Summary: | 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significance of Arabic handwriting recognition across various domains, such as document digitization, archival systems, historical document analysis, and language learning, particularly among toddlers.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. |
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