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Identifying cross-depicted historical motifs
Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
Staatsbibliothek zu Berlin, Preußischer Kulturbesitz, Berlin, Germany.
Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
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2018 (English)In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 333-338, article id 8583783Conference paper, Published paper (Refereed)
Abstract [en]

Cross-depiction is the problem of identifying the same object even when it is depicted in a variety of manners.This is a common problem in handwritten historical document image analysis, for instance when the same letter or motif is depicted in several different ways. It is a simple task for humans yet conventional computer vision methods struggle to cope with it. In this paper we address this problem using state-of-the-art deep learning techniques on a dataset of historical watermarks containing images created with different methods of reproduction, such as hand tracing, rubbing, and radiography.To study the robustness of deep learning based approaches to the cross-depiction problem, we measure their performance on two different tasks: Classification and similarity rankings. For the former we achieve a classification accuracy of 96 % using deep convolutional neural networks. For the latter we have a false positive rate at 95% recall of 0.11. These results outperform state-of-the-art methods by a significant margin

Place, publisher, year, edition, pages
IEEE, 2018. p. 333-338, article id 8583783
Series
International Conference on Handwriting Recognition, ISSN 2167-6445
Keywords [en]
convolutional neural network, cross-depiction, deep learning, machine learning, watermarks
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-72981DOI: 10.1109/ICFHR-2018.2018.00065ISI: 000454983200056Scopus ID: 2-s2.0-85060032898ISBN: 978-1-5386-5875-8 (print)OAI: oai:DiVA.org:ltu-72981DiVA, id: diva2:1290886
Conference
16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, 5- August 2018, Niagara Fall, United States
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-03-11Bibliographically approved

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Liwicki, Marcus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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