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A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay lang...

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Main Authors: Al-Boeridi, Omar N., Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Koh, S. P.
Format: Article
Language:English
Published: Springer 2015
Online Access:http://psasir.upm.edu.my/id/eprint/43600/1/A%20scalable%20hybrid%20decision%20system%20%28HDS%29%20for%20Roman%20word%20recognition%20using%20ANN%20SVM%20Study%20case%20on%20Malay%20word%20recognition.pdf
http://psasir.upm.edu.my/id/eprint/43600/
http://fcb991b696f563270c39464d67d2c3bd.proxysheep.com/article/10.1007/s00521-015-1824-0
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spelling my.upm.eprints.436002016-07-21T09:41:49Z http://psasir.upm.edu.my/id/eprint/43600/ A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition Al-Boeridi, Omar N. Syed Ahmad Abdul Rahman, Sharifah Mumtazah Koh, S. P. An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications. Springer 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43600/1/A%20scalable%20hybrid%20decision%20system%20%28HDS%29%20for%20Roman%20word%20recognition%20using%20ANN%20SVM%20Study%20case%20on%20Malay%20word%20recognition.pdf Al-Boeridi, Omar N. and Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Koh, S. P. (2015) A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition. Neural Computing & Applications, 26 (6). pp. 1505-1513. ISSN 0941-0643; ESSN: 1433-3058 http://fcb991b696f563270c39464d67d2c3bd.proxysheep.com/article/10.1007/s00521-015-1824-0 10.1007/s00521-015-1824-0
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.
format Article
author Al-Boeridi, Omar N.
Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Koh, S. P.
spellingShingle Al-Boeridi, Omar N.
Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Koh, S. P.
A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
author_facet Al-Boeridi, Omar N.
Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Koh, S. P.
author_sort Al-Boeridi, Omar N.
title A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
title_short A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
title_full A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
title_fullStr A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
title_full_unstemmed A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition
title_sort scalable hybrid decision system (hds) for roman word recognition using ann svm: study case on malay word recognition
publisher Springer
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/43600/1/A%20scalable%20hybrid%20decision%20system%20%28HDS%29%20for%20Roman%20word%20recognition%20using%20ANN%20SVM%20Study%20case%20on%20Malay%20word%20recognition.pdf
http://psasir.upm.edu.my/id/eprint/43600/
http://fcb991b696f563270c39464d67d2c3bd.proxysheep.com/article/10.1007/s00521-015-1824-0
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score 13.211869

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