Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam

This study aims to develop a Chinese character recognition system using Convolutional Neural Network (CNN) system that is specifically designed for the challenging task of recognizing Chinese characters, taking into consideration the complex structures and various styles of writing that these charac...

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Main Author:	Mat Sam, Nur Fatihah Hatini
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95736/1/95736.pdf
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id	my-uitm-ir.95736
record_format	uketd_dc
spelling	my-uitm-ir.957362024-05-23T03:20:39Z Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam 2024 Mat Sam, Nur Fatihah Hatini Neural networks (Computer science) This study aims to develop a Chinese character recognition system using Convolutional Neural Network (CNN) system that is specifically designed for the challenging task of recognizing Chinese characters, taking into consideration the complex structures and various styles of writing that these characters possess. With the Chinese language's global importance, particularly in places like Malaysia with one of the large Chinese communities, there is a strong need for an effective and user-friendly character recognition system. The CNN system is remarkably accurate, with a recognition rate of 99.99% for Chinese character recognition and a training accuracy of 92%. The research includes an in-depth examination of CNN requirements, the methodical building of a recognition system, and an accurate evaluation of its performance using testing measures such as precision, recall, and F1-score. The results indicate impressive testing accuracy with minimal loss, demonstrating the system's ability to generalize to newly collected data. The confusion matrix provides an in-depth analysis of the model's classification accuracy over 135 different Chinese character classes. Functionality testing verifies the system's ability to accommodate different handwriting styles, increasing its value for learners of varying competence levels. Recognizing limitations due to the intentional focus on recognizable characters for beginners, the study proposes future directions, such as investigating advanced CNN architectures, augmenting training datasets, incorporating transfer learning strategies, and incorporating user feedback for continuous refinement. In short, this initiative aims to bridge the gap between technological breakthroughs in CNNs and evolving educational objectives for Chinese character recognition, therefore contributing to the larger conversation about the connection of technology and education in linguistic domains. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95736/ https://ir.uitm.edu.my/id/eprint/95736/1/95736.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Raju, Rajeswari
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Raju, Rajeswari
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mat Sam, Nur Fatihah Hatini Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
description	This study aims to develop a Chinese character recognition system using Convolutional Neural Network (CNN) system that is specifically designed for the challenging task of recognizing Chinese characters, taking into consideration the complex structures and various styles of writing that these characters possess. With the Chinese language's global importance, particularly in places like Malaysia with one of the large Chinese communities, there is a strong need for an effective and user-friendly character recognition system. The CNN system is remarkably accurate, with a recognition rate of 99.99% for Chinese character recognition and a training accuracy of 92%. The research includes an in-depth examination of CNN requirements, the methodical building of a recognition system, and an accurate evaluation of its performance using testing measures such as precision, recall, and F1-score. The results indicate impressive testing accuracy with minimal loss, demonstrating the system's ability to generalize to newly collected data. The confusion matrix provides an in-depth analysis of the model's classification accuracy over 135 different Chinese character classes. Functionality testing verifies the system's ability to accommodate different handwriting styles, increasing its value for learners of varying competence levels. Recognizing limitations due to the intentional focus on recognizable characters for beginners, the study proposes future directions, such as investigating advanced CNN architectures, augmenting training datasets, incorporating transfer learning strategies, and incorporating user feedback for continuous refinement. In short, this initiative aims to bridge the gap between technological breakthroughs in CNNs and evolving educational objectives for Chinese character recognition, therefore contributing to the larger conversation about the connection of technology and education in linguistic domains.
format	Thesis
qualification_level	Bachelor degree
author	Mat Sam, Nur Fatihah Hatini
author_facet	Mat Sam, Nur Fatihah Hatini
author_sort	Mat Sam, Nur Fatihah Hatini
title	Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
title_short	Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
title_full	Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
title_fullStr	Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
title_full_unstemmed	Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam
title_sort	chinese character recognition system using convolutional neural network (cnn) / nur fatihah hatini mat sam
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95736/1/95736.pdf
_version_	1804889973233549312

Chinese character recognition system using Convolutional Neural Network (CNN) / Nur Fatihah Hatini Mat Sam

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