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Article: Feature selection in the recognition of handwritten Chinese characters

TitleFeature selection in the recognition of handwritten Chinese characters
Authors
KeywordsCharacter stroke features
Chinese OCR
Feature extraction
Karhunen-Loeve analysis
Stroke density
Issue Date1997
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 1997, v. 10 n. 5, p. 495-502 How to Cite?
AbstractA method is proposed here to extract appropriate features for the recognition of handwritten Chinese characters. The features represent the lengths, positions and directions of the character strokes. In addition, two approaches (Karhunen-Loeve and stroke density analyses) have been used to analyze the information content of Chinese characters, from which a cost-effective, non-uniform, and two-dimensional sampling scheme for feature extraction has been derived. The resulting scheme extracts more samples from regions of higher information content. A recognition system that combines all the proposed approaches was built, and experiments were performed on the 500 most frequently used Chinese character classes, with 20,000 handwritten samples. Results indicated that the proposed methods are useful. © 1997 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73716
ISSN
2015 Impact Factor: 2.368
2015 SCImago Journal Rankings: 1.371
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, CHen_HK
dc.contributor.authorSze, LSen_HK
dc.date.accessioned2010-09-06T06:54:04Z-
dc.date.available2010-09-06T06:54:04Z-
dc.date.issued1997en_HK
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 1997, v. 10 n. 5, p. 495-502en_HK
dc.identifier.issn0952-1976en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73716-
dc.description.abstractA method is proposed here to extract appropriate features for the recognition of handwritten Chinese characters. The features represent the lengths, positions and directions of the character strokes. In addition, two approaches (Karhunen-Loeve and stroke density analyses) have been used to analyze the information content of Chinese characters, from which a cost-effective, non-uniform, and two-dimensional sampling scheme for feature extraction has been derived. The resulting scheme extracts more samples from regions of higher information content. A recognition system that combines all the proposed approaches was built, and experiments were performed on the 500 most frequently used Chinese character classes, with 20,000 handwritten samples. Results indicated that the proposed methods are useful. © 1997 Elsevier Science Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_HK
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_HK
dc.subjectCharacter stroke featuresen_HK
dc.subjectChinese OCRen_HK
dc.subjectFeature extractionen_HK
dc.subjectKarhunen-Loeve analysisen_HK
dc.subjectStroke densityen_HK
dc.titleFeature selection in the recognition of handwritten Chinese charactersen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0952-1976&volume=10&spage=495&epage=502&date=1997&atitle=Feature+Selection+in+The+Recognition+of+Handwritten+Chinese+Charactersen_HK
dc.identifier.emailLeung, CH:chleung@eee.hku.hken_HK
dc.identifier.authorityLeung, CH=rp00146en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0031247987en_HK
dc.identifier.hkuros34144en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031247987&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.issue5en_HK
dc.identifier.spage495en_HK
dc.identifier.epage502en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLeung, CH=7402612415en_HK
dc.identifier.scopusauthoridSze, LS=6602158907en_HK

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