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Neural network-based efficient measurement method on upside down orientation of a digital document

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dc.contributor.authorShin, Y.-
dc.contributor.authorCho, Y.-
dc.contributor.authorKang, H.W.-
dc.contributor.authorKang, J.-G.-
dc.contributor.authorJung, J.-W.-
dc.date.accessioned2023-04-28T00:41:10Z-
dc.date.available2023-04-28T00:41:10Z-
dc.date.issued2020-04-
dc.identifier.issn2415-6698-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/7106-
dc.description.abstractAs many digital documents are required in various environments, paper documents are digitized by scanner, fax, digital camera and specific software. In the case of a scanned document, we need to check whether the document is right sided or upside down because the orientation of the scanned document is determined by the orientation in which the paper document is placed. It is time-consuming for a person to check all the documents whether they are upside down. We propose an algorithm that can automatically determine upside down documents. The proposed artificial neural network-based method shows a high accuracy and efficiency in time for general documents. In addition, OCR-based method and CNN-based method were used to compare with the performance of the proposed method. © 2020 ASTES Publishers. All rights reserved.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherASTES Publishers-
dc.titleNeural network-based efficient measurement method on upside down orientation of a digital document-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.25046/aj050286-
dc.identifier.scopusid2-s2.0-85084832703-
dc.identifier.bibliographicCitationAdvances in Science, Technology and Engineering Systems, v.5, no.2, pp 697 - 702-
dc.citation.titleAdvances in Science, Technology and Engineering Systems-
dc.citation.volume5-
dc.citation.number2-
dc.citation.startPage697-
dc.citation.endPage702-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorArtificial Neural Network-
dc.subject.keywordAuthorConvolutional Neural Network-
dc.subject.keywordAuthorOptical Character Recognition-
dc.subject.keywordAuthorUpside Down Detection-
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