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dc.creatoramelec, viloria
dc.creatorRico, Reinaldo
dc.creatorPineda, Omar
dc.date.accessioned2020-11-11T22:46:13Z
dc.date.available2020-11-11T22:46:13Z
dc.date.issued2020
dc.identifier.issn2194-5357
dc.identifier.urihttps://hdl.handle.net/11323/7271
dc.description.abstractOCR (Optical Character Recognition) is a line of research within image processing for which many techniques and methodologies have been developed. Set of pixels recognized based on the digitalized image and this study presents an iterative process that consists of five phases of the OCR. For this purpose, several image processing methods are applied, as well as two variable selection methods, and several supervised automated learning methods are explored. Among the classification models, those of deep learning stand out for their novelty and enormous potential.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectClassification modelsspa
dc.subjectGenetic algorithmspa
dc.subjectImage processingspa
dc.subjectMethodsspa
dc.subjectRecognition of handwritten digitsspa
dc.titleRecognition of handwritten digits by image processing methods and classification modelsspa
dc.typePreprintspa
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dc.type.hasVersioninfo:eu-repo/semantics/draftspa
dc.source.urlhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85089245515&doi=10.1007%2f978-3-030-51859-2_2&origin=inward&txGid=1f85feb85a61963014477b3db2a85ca2spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-05-07


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