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https://doi.org/10.21256/zhaw-1533| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Fully convolutional neural networks for newspaper article segmentation |
| Autor/-in: | Meier, Benjamin Stadelmann, Thilo Stampfli, Jan Arnold, Marek Cieliebak, Mark |
| DOI: | 10.21256/zhaw-1533 |
| Tagungsband: | Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) |
| Angaben zur Konferenz: | 14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), Kyoto Japan, 13-15 November 2017 |
| Erscheinungsdatum: | 2017 |
| Verlag / Hrsg. Institution: | CPS |
| Verlag / Hrsg. Institution: | Kyoto |
| Sprache: | Englisch |
| Schlagwörter: | Semantic segmentation; CNN; Deep learning; Datalab |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Segmenting 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/1863 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 212962.pdf | 523.17 kB | Adobe PDF |  Öffnen/Anzeigen |
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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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