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

Autor(en): Garita Figueiredo, R.
Kühnberger, K.-U. 
Pipa, G. 
Thelen, T. 
Herausgeber: Tetko, I.V.
Karpov, P.
Theis, F.
Kurkova, V.
Stichwörter: Copyrights; Information retrieval systems; Neural networks, Classification process; Convolutional networks; Document metadatas; Document structure; Feature-based classification; Portable document formats; Semi-supervised; Structural feature, Deep learning
Erscheinungsdatum: 2019
Herausgeber: Springer Verlag
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen: 11730 LNCS
Startseite: 69
Seitenende: 75
Zusammenfassung: 
This 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.
Beschreibung: 
Conference of 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference Date: 17 September 2019 Through 19 September 2019; Conference Code:231689
ISBN: 9783030304898
ISSN: 03029743
DOI: 10.1007/978-3-030-30490-4_7
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072869152&doi=10.1007%2f978-3-030-30490-4_7&partnerID=40&md5=457e4d59c82d15cc22a4dc99e34c692f

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