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Conference Paper: Character segmentation algorithm for off-line handwritten script recognition

TitleCharacter segmentation algorithm for off-line handwritten script recognition
Authors
Yung, NHCLai, AHSChua, PZP
KeywordsHandwritten script
Character segmentation
Geometric classes
X-axis projection
Y-axis projection
Issue Date1995
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
Visual communications and image processing, Taipei, Taiwan, China, 24-26 May 1995. In Proceedings of SPIE, v. 2501 n. 3, p. 1656-1667 How to Cite?
DOI: http://dx.doi.org/10.1117/12.206702
AbstractIn this paper, a new character segmentation algorithm for dealing with off-line handwritten script recognition is presented. The X-axis projection, Y-axis projection and geometric classes techniques used by the algorithm proves to be successful in segmenting normal handwriting with a success rate of 93.5%. As a result of this development, detailed understanding of geometric classes of English characters and the difficult cases in segmentation was gained. Although the algorithm works quite well with a randomly chosen sample, results of a detailed analysis may shed new light into the tuning of the algorithm especially for segmenting the identified difficult cases.
Persistent Identifierhttp://hdl.handle.net/10722/45974
ISSN
0277-786X
2020 SCImago Journal Rankings: 0.192

 

DC FieldValueLanguage
dc.contributor.authorYung, NHCen_HK
dc.contributor.authorLai, AHSen_HK
dc.contributor.authorChua, PZPen_HK
dc.date.accessioned2007-10-30T06:39:47Z-
dc.date.available2007-10-30T06:39:47Z-
dc.date.issued1995en_HK
dc.identifier.citationVisual communications and image processing, Taipei, Taiwan, China, 24-26 May 1995. In Proceedings of SPIE, v. 2501 n. 3, p. 1656-1667en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/45974-
dc.description.abstractIn this paper, a new character segmentation algorithm for dealing with off-line handwritten script recognition is presented. The X-axis projection, Y-axis projection and geometric classes techniques used by the algorithm proves to be successful in segmenting normal handwriting with a success rate of 93.5%. As a result of this development, detailed understanding of geometric classes of English characters and the difficult cases in segmentation was gained. Although the algorithm works quite well with a randomly chosen sample, results of a detailed analysis may shed new light into the tuning of the algorithm especially for segmenting the identified difficult cases.en_HK
dc.format.extent476501 bytes-
dc.format.extent10863 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 1995 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/10.1117/12.206702-
dc.subjectHandwritten scripten_HK
dc.subjectCharacter segmentationen_HK
dc.subjectGeometric classesen_HK
dc.subjectX-axis projectionen_HK
dc.subjectY-axis projectionen_HK
dc.titleCharacter segmentation algorithm for off-line handwritten script recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYung, Nelson H:nyung@eee.hku.hk-
dc.identifier.authorityYung, Nelson H=rp00226-
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1117/12.206702en_HK
dc.identifier.scopuseid_2-s2.0-0029214991-
dc.identifier.hkuros1814-
dc.identifier.volume2501-
dc.identifier.issue3-
dc.identifier.spage1656-
dc.identifier.epage1667-
dc.identifier.issnl0277-786X-

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