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International Workshop on Document Analysis Systems

DAS 2022: Document Analysis Systems pp 583–597Cite as

A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13237))

Abstract

Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images.

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Notes

  1. 1.

    XAC is a governmental archivist institution with url for further detail: https://xac.gencat.cat/en/inici/.

  2. 2.

    mAP approximated from training bacthes.

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Acknowledgment

This work has been partially supported by the Spanish projects RTI2018-095645-B-C21, and FCT-19-15244, and the Catalan projects 2017-SGR-1783, the Culture Department of the Generalitat de Catalunya, and the CERCA Program/Generalitat de Catalunya.

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Authors and Affiliations

  1. Computer Vision Center and Computer Science Department, Universitat Autònoma de Barcelona, Bellaterra/Cerdanyola, Catalunya, Spain

    Adrià Molina, Lluis Gomez, Oriol Ramos Terrades & Josep Lladós

Authors
  1. Adrià Molina
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  2. Lluis Gomez
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  3. Oriol Ramos Terrades
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  4. Josep Lladós
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Corresponding author

Correspondence to Adrià Molina .

Editor information

Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Seiichi Uchida

  2. Boise State University, BOISE, ID, USA

    Elisa Barney

  3. LIRIS UMR CNRS, Villeurbanne, France

    Véronique Eglin

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Molina, A., Gomez, L., Ramos Terrades, O., Lladós, J. (2022). A Generic Image Retrieval Method for Date Estimation of Historical Document Collections. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_39

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06554-5

  • Online ISBN: 978-3-031-06555-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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