Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23103
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorChu, K-
dc.creatorChi, Z-
dc.creatorSiu, W-
dc.date.accessioned2014-12-19T06:55:14Z-
dc.date.available2014-12-19T06:55:14Z-
dc.identifier.issn1022-4653-
dc.identifier.urihttp://hdl.handle.net/10397/23103-
dc.language.isoenen_US
dc.subjectDocument image processingen_US
dc.subjectNeural network applicationsen_US
dc.subjectTexture analysisen_US
dc.subjectWritten language separationen_US
dc.titleA neural network approach for language separation of textual document imagesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage381-
dc.identifier.epage386-
dc.identifier.volume7-
dc.identifier.issue4-
dcterms.abstractIn this paper, a neural network approach is presented to classify grey scale Chinese and English document images. The approach, which consists of three steps: preprocessing, feature extraction and classification, can successfully handle Chinese and English document images of different densities, fonts, sizes and styles of characters. Two neural networks are employed. The first neural network is used to derive a set of 15 masks for extracting features. The coefficients of the masks are approximated to a set of computationally-simple values so that the computational complexity in extracting features can be reduced significantly. The second neural network of a smaller size is then trained using the extracted 15 features to perform the language separation. Experimental results on a set of 40 document images including 20 Chinese document images and 20 English document images show that 100% correct classification rate can be achieved. Our approach is compared favorably with an existing language separation method.-
dcterms.bibliographicCitationChinese journal of electronics, 1998, v. 7, no. 4, p. 381-386-
dcterms.isPartOfChinese Journal of Electronics-
dcterms.issued1998-
dc.identifier.scopus2-s2.0-0032179071-
Appears in Collections:Journal/Magazine Article
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
Citations as of Aug 18, 2020

Page view(s)

162
Last Week
1
Last month
Citations as of Oct 21, 2020

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.