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Conference Paper: Different approach to designing neural networks for similar handwritten Chinese character recognition

TitleDifferent approach to designing neural networks for similar handwritten Chinese character recognition
Authors
KeywordsHandwritten Chinese character recognition
Neural networks
ETL9B
Issue Date1998
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedings
Citation
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, San Diego, California, USA, 20-22 July 1998. In Proceedings of SPIE, 1998, v. 3455, p. 286-291 How to Cite?
AbstractThe input images of Chinese characters are normally preprocessed using different image processing techniques before the main classification in the handwritten Chinese character recognition. The authors proposed a different approach to the system philosophy of solving the handwritten Chinese character recognition problem where no preprocessing is necessary. The Chinese characters are treated as ideographs. The proposed system consists of a Rough Classifier which trigger the different Fine Classifiers. Each classifier is an optimized artificial neural network using genetic algorithms. A reduced system has been implemented. The result shows that the proposed system has higher recognition rate than the similar systems reported and is more efficiency.
Persistent Identifierhttp://hdl.handle.net/10722/46583
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYip, HFDen_HK
dc.contributor.authorYu, WWHen_HK
dc.date.accessioned2007-10-30T06:53:26Z-
dc.date.available2007-10-30T06:53:26Z-
dc.date.issued1998en_HK
dc.identifier.citationApplications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, San Diego, California, USA, 20-22 July 1998. In Proceedings of SPIE, 1998, v. 3455, p. 286-291-
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/46583-
dc.description.abstractThe input images of Chinese characters are normally preprocessed using different image processing techniques before the main classification in the handwritten Chinese character recognition. The authors proposed a different approach to the system philosophy of solving the handwritten Chinese character recognition problem where no preprocessing is necessary. The Chinese characters are treated as ideographs. The proposed system consists of a Rough Classifier which trigger the different Fine Classifiers. Each classifier is an optimized artificial neural network using genetic algorithms. A reduced system has been implemented. The result shows that the proposed system has higher recognition rate than the similar systems reported and is more efficiency.en_HK
dc.format.extent251650 bytes-
dc.format.extent3380 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/app/Publications/index.cfm?fuseaction=proceedingsen_HK
dc.relation.ispartofProceedings of SPIE-
dc.rightsCopyright 1998 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. This article is available online at https://doi.org/10.1117/12.326723-
dc.subjectHandwritten Chinese character recognitionen_HK
dc.subjectNeural networksen_HK
dc.subjectETL9Ben_HK
dc.titleDifferent approach to designing neural networks for similar handwritten Chinese character recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-786X&volume=3455&spage=286&epage=291&date=1998&atitle=Different+approach+to+designing+neural+networks+for+similar+handwritten+Chinese+character+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1117/12.326723en_HK
dc.identifier.scopuseid_2-s2.0-58149234055-
dc.identifier.hkuros47043-

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