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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1533
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dc.contributor.authorMeier, Benjamin-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorStampfli, Jan-
dc.contributor.authorArnold, Marek-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2017-12-15T07:48:50Z-
dc.date.available2017-12-15T07:48:50Z-
dc.date.issued2017-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1863-
dc.description.abstractSegmenting newspaper pages into articles that semantically belong together is a necessary prerequisite for article-based information retrieval on print media collections like e.g. archives and libraries. It is challenging due to vastly differing layouts of papers, various content types and different languages, but commercially very relevant for e.g. media monitoring.  We present a semantic segmentation approach based on the visual appearance of each page. We apply a fully convolutional neural network (FCN) that we train in an end-to-end fashion to transform the input image into a segmentation mask in one pass. We show experimentally that the FCN performs very well: it outperforms a deep learning-based commercial solution by a large margin in terms of segmentation quality while in addition being computationally two orders of magnitude more efficient.de_CH
dc.language.isoende_CH
dc.publisherCPSde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSemantic segmentationde_CH
dc.subjectCNNde_CH
dc.subjectDeep learningde_CH
dc.subjectDatalabde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleFully convolutional neural networks for newspaper article segmentationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeKyotode_CH
dc.identifier.doi10.21256/zhaw-1533-
zhaw.conference.details14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), Kyoto Japan, 13-15 November 2017de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
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Meier, B., Stadelmann, T., Stampfli, J., Arnold, M., & Cieliebak, M. (2017). Fully convolutional neural networks for newspaper article segmentation. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). https://doi.org/10.21256/zhaw-1533
Meier, B. et al. (2017) ‘Fully convolutional neural networks for newspaper article segmentation’, in Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Kyoto: CPS. Available at: https://doi.org/10.21256/zhaw-1533.
B. Meier, T. Stadelmann, J. Stampfli, M. Arnold, and M. Cieliebak, “Fully convolutional neural networks for newspaper article segmentation,” in Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017. doi: 10.21256/zhaw-1533.
MEIER, Benjamin, Thilo STADELMANN, Jan STAMPFLI, Marek ARNOLD und Mark CIELIEBAK, 2017. Fully convolutional neural networks for newspaper article segmentation. In: Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Conference paper. Kyoto: CPS. 2017
Meier, Benjamin, Thilo Stadelmann, Jan Stampfli, Marek Arnold, and Mark Cieliebak. 2017. “Fully Convolutional Neural Networks for Newspaper Article Segmentation.” Conference paper. In Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Kyoto: CPS. https://doi.org/10.21256/zhaw-1533.
Meier, Benjamin, et al. “Fully Convolutional Neural Networks for Newspaper Article Segmentation.” Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), CPS, 2017, https://doi.org/10.21256/zhaw-1533.


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