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

Backpropagation; Feature extraction; Neural networks; Optical character recognition; Support vector machines; Vocabulary control; ANN; Decision systems; Gravity centers; Offline handwriting recognition; SVM; Character recognition

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Main Authors: Al-Boeridi O.N., Syed Ahmad S.M., Koh S.P.
Other Authors: 56507369200
Format: Article
Published: Springer-Verlag London Ltd 2023
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spelling my.uniten.dspace-222852023-05-29T14:00:00Z A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition Al-Boeridi O.N. Syed Ahmad S.M. Koh S.P. 56507369200 24721182400 22951210700 Backpropagation; Feature extraction; Neural networks; Optical character recognition; Support vector machines; Vocabulary control; ANN; Decision systems; Gravity centers; Offline handwriting recognition; SVM; Character 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 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. � 2015, The Natural Computing Applications Forum. Final 2023-05-29T06:00:00Z 2023-05-29T06:00:00Z 2015 Article 10.1007/s00521-015-1824-0 2-s2.0-84937971328 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937971328&doi=10.1007%2fs00521-015-1824-0&partnerID=40&md5=c96ab589b4d45db53fe7a8ef4bb51b8c https://irepository.uniten.edu.my/handle/123456789/22285 26 6 1505 1513 All Open Access, Green Springer-Verlag London Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Backpropagation; Feature extraction; Neural networks; Optical character recognition; Support vector machines; Vocabulary control; ANN; Decision systems; Gravity centers; Offline handwriting recognition; SVM; Character recognition
author2 56507369200
author_facet 56507369200
Al-Boeridi O.N.
Syed Ahmad S.M.
Koh S.P.
format Article
author Al-Boeridi O.N.
Syed Ahmad S.M.
Koh S.P.
spellingShingle Al-Boeridi O.N.
Syed Ahmad S.M.
Koh S.P.
A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition
author_sort Al-Boeridi O.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-Verlag London Ltd
publishDate 2023
_version_ 1806428286753964032
score 13.211869

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