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Chinese character recognition using support vector machine

Optical character recognition is the art of scanning and detecting the word in the images so that the machine can identify and classify the character. Chinese characters are one of the world's most widely used writing systems. It is used by more than one-quarter of the world’s population in dai...

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Main Authors: Baharum, Aslina, Ismail, Rozita, A. Wahab, Shaliza Hayati, Deris, Farhana Diana, Mat Noor, Noorsidi Aizuddin, Mohd. Kasihmuddin, Mohd. Shareduwan
Format: Article
Language:English
Published: Little Lion Scientific 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/102580/1/FarhanaDianaDeris2022_ChineseCharacterRecognitionusingSupport.pdf
http://eprints.utm.my/id/eprint/102580/
http://www.jatit.org/volumes/Vol100No17/2Vol100No17.pdf
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spelling my.utm.1025802023-09-09T01:36:12Z http://eprints.utm.my/id/eprint/102580/ Chinese character recognition using support vector machine Baharum, Aslina Ismail, Rozita A. Wahab, Shaliza Hayati Deris, Farhana Diana Mat Noor, Noorsidi Aizuddin Mohd. Kasihmuddin, Mohd. Shareduwan QA75 Electronic computers. Computer science Optical character recognition is the art of scanning and detecting the word in the images so that the machine can identify and classify the character. Chinese characters are one of the world's most widely used writing systems. It is used by more than one-quarter of the world’s population in daily communication. Chinese characters can be considered difficult because they have many categories, complex character structures, similarities between characters, and various fonts or writing styles. There are many known machine learning algorithms for character recognition, but not all can classify Chinese characters with high speed and accuracy. Therefore, this paper proposes recognizing Chinese characters using support vector machines. Support vector machines are a classification of two classes widely used in classification. It produces very accurate results for many classes, making it suitable for recognizing Chinese characters. Little Lion Scientific 2022-09-15 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/102580/1/FarhanaDianaDeris2022_ChineseCharacterRecognitionusingSupport.pdf Baharum, Aslina and Ismail, Rozita and A. Wahab, Shaliza Hayati and Deris, Farhana Diana and Mat Noor, Noorsidi Aizuddin and Mohd. Kasihmuddin, Mohd. Shareduwan (2022) Chinese character recognition using support vector machine. Journal of Theoretical and Applied Information Technology, 100 (17). 5335 -5340. ISSN 1992-8645 http://www.jatit.org/volumes/Vol100No17/2Vol100No17.pdf NA
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Baharum, Aslina
Ismail, Rozita
A. Wahab, Shaliza Hayati
Deris, Farhana Diana
Mat Noor, Noorsidi Aizuddin
Mohd. Kasihmuddin, Mohd. Shareduwan
Chinese character recognition using support vector machine
description Optical character recognition is the art of scanning and detecting the word in the images so that the machine can identify and classify the character. Chinese characters are one of the world's most widely used writing systems. It is used by more than one-quarter of the world’s population in daily communication. Chinese characters can be considered difficult because they have many categories, complex character structures, similarities between characters, and various fonts or writing styles. There are many known machine learning algorithms for character recognition, but not all can classify Chinese characters with high speed and accuracy. Therefore, this paper proposes recognizing Chinese characters using support vector machines. Support vector machines are a classification of two classes widely used in classification. It produces very accurate results for many classes, making it suitable for recognizing Chinese characters.
format Article
author Baharum, Aslina
Ismail, Rozita
A. Wahab, Shaliza Hayati
Deris, Farhana Diana
Mat Noor, Noorsidi Aizuddin
Mohd. Kasihmuddin, Mohd. Shareduwan
author_facet Baharum, Aslina
Ismail, Rozita
A. Wahab, Shaliza Hayati
Deris, Farhana Diana
Mat Noor, Noorsidi Aizuddin
Mohd. Kasihmuddin, Mohd. Shareduwan
author_sort Baharum, Aslina
title Chinese character recognition using support vector machine
title_short Chinese character recognition using support vector machine
title_full Chinese character recognition using support vector machine
title_fullStr Chinese character recognition using support vector machine
title_full_unstemmed Chinese character recognition using support vector machine
title_sort chinese character recognition using support vector machine
publisher Little Lion Scientific
publishDate 2022
url http://eprints.utm.my/id/eprint/102580/1/FarhanaDianaDeris2022_ChineseCharacterRecognitionusingSupport.pdf
http://eprints.utm.my/id/eprint/102580/
http://www.jatit.org/volumes/Vol100No17/2Vol100No17.pdf
_version_ 1778160751323643904
score 13.211869

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