Vietnamese character recognition based on cnn model with reduced character classes

This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edg...

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Main Authors: Phan, T.H., Tran, D.C., Hassan, M.F.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23853 /
Published: Institute of Advanced Engineering and Science 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102988809&doi=10.11591%2feei.v10i2.2810&partnerID=40&md5=5e693f828b523a177314ee98545d5589
http://eprints.utp.edu.my/23853/
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spelling utp-eprints.238532021-08-19T13:08:44Z Vietnamese character recognition based on cnn model with reduced character classes Phan, T.H. Tran, D.C. Hassan, M.F. This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as �c� and �C�, �o� and �O�. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness. © 2021, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102988809&doi=10.11591%2feei.v10i2.2810&partnerID=40&md5=5e693f828b523a177314ee98545d5589 Phan, T.H. and Tran, D.C. and Hassan, M.F. (2021) Vietnamese character recognition based on cnn model with reduced character classes. Bulletin of Electrical Engineering and Informatics, 10 (2). pp. 962-969. http://eprints.utp.edu.my/23853/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as �c� and �C�, �o� and �O�. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness. © 2021, Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Phan, T.H.
Tran, D.C.
Hassan, M.F.
spellingShingle Phan, T.H.
Tran, D.C.
Hassan, M.F.
Vietnamese character recognition based on cnn model with reduced character classes
author_sort Phan, T.H.
title Vietnamese character recognition based on cnn model with reduced character classes
title_short Vietnamese character recognition based on cnn model with reduced character classes
title_full Vietnamese character recognition based on cnn model with reduced character classes
title_fullStr Vietnamese character recognition based on cnn model with reduced character classes
title_full_unstemmed Vietnamese character recognition based on cnn model with reduced character classes
title_sort vietnamese character recognition based on cnn model with reduced character classes
publisher Institute of Advanced Engineering and Science
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102988809&doi=10.11591%2feei.v10i2.2810&partnerID=40&md5=5e693f828b523a177314ee98545d5589
http://eprints.utp.edu.my/23853/
_version_ 1741196743259717632
score 11.62408

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