Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents

DC FieldValueLanguage
dc.contributor.authorGarita Figueiredo, R.
dc.contributor.authorKühnberger, K.-U.
dc.contributor.authorPipa, G.
dc.contributor.authorThelen, T.
dc.contributor.editorTetko, I.V.
dc.contributor.editorKarpov, P.
dc.contributor.editorTheis, F.
dc.contributor.editorKurkova, V.
dc.date.accessioned2021-12-23T16:33:37Z-
dc.date.available2021-12-23T16:33:37Z-
dc.date.issued2019
dc.identifier.isbn9783030304898
dc.identifier.issn03029743
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/17783-
dc.descriptionConference of 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference Date: 17 September 2019 Through 19 September 2019; Conference Code:231689
dc.description.abstractThis document describes the implementation of a copyright classification process for user-contributed Portable Document Format (PDF) documents. The implementation employs two ways to classify documents as copyright-protected or non-copyright-protected: first, using structural features extracted from the document metadata, content and underlying document structure; and second, by turning the documents into images and using their pixels to generate features for semi-supervised deep convolutional networks. © 2019, Springer Nature Switzerland AG.
dc.language.isoen
dc.publisherSpringer Verlag
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subjectCopyrights
dc.subjectInformation retrieval systems
dc.subjectNeural networks, Classification process
dc.subjectConvolutional networks
dc.subjectDocument metadatas
dc.subjectDocument structure
dc.subjectFeature-based classification
dc.subjectPortable document formats
dc.subjectSemi-supervised
dc.subjectStructural feature, Deep learning
dc.titleCombining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents
dc.typeconference paper
dc.identifier.doi10.1007/978-3-030-30490-4_7
dc.identifier.scopus2-s2.0-85072869152
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072869152&doi=10.1007%2f978-3-030-30490-4_7&partnerID=40&md5=457e4d59c82d15cc22a4dc99e34c692f
dc.description.volume11730 LNCS
dc.description.startpage69
dc.description.endpage75
dcterms.isPartOf.abbreviationLect. Notes Comput. Sci.
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptZentrum VirtUOS-
crisitem.author.deptidinstitute28-
crisitem.author.deptidinstitute28-
crisitem.author.deptidorganisation31-
crisitem.author.orcid0000-0003-1626-0598-
crisitem.author.orcid0000-0002-3416-2652-
crisitem.author.orcid0000-0002-3337-6093-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidKuKa032-
crisitem.author.netidPiGo340-
crisitem.author.netidThTo467-
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