eprintid: 15823
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/58/23
datestamp: 2023-11-10 03:30:27
lastmod: 2023-11-10 03:30:27
status_changed: 2023-11-10 02:00:30
type: article
metadata_visibility: show
creators_name: Phan, T.H.
creators_name: Tran, D.C.
creators_name: Hassan, M.F.
title: Vietnamese character recognition based on cnn model with reduced character classes
ispublished: pub
note: cited By 4
abstract: 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.
date: 2021
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102988809&doi=10.11591%2feei.v10i2.2810&partnerID=40&md5=5e693f828b523a177314ee98545d5589
id_number: 10.11591/eei.v10i2.2810
full_text_status: none
publication: Bulletin of Electrical Engineering and Informatics
volume: 10
number: 2
pagerange: 962-969
refereed: TRUE
issn: 20893191
citation:   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.  ISSN 20893191