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Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation

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Discrete Geometry and Mathematical Morphology (DGMM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12708))

Abstract

The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.

Extra material for this paper (full-size figures, results, code, dataset) available at: https://github.com/soduco/paper-dgmm2021.

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Acknowledgements

This work was partially funded by the French National Research Agency (ANR): Project SoDuCo, grant ANR-18-CE38-0013. We would also like to thank the anonymous reviewers for their valuable feedback. The authors are grateful to the Bibliothèque de l’Hôtel de Ville (BHdV) and the City of Paris for their support for giving us access to the raw map images.

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Authors and Affiliations

  1. EPITA Research and Development Lab. (LRDE), EPITA, Le Kremlin-Bicêtre, France

    Yizi Chen, Edwin Carlinet & Joseph Chazalon

  2. Univ. Gustave Eiffel, IGN-ENSG, LaSTIG, Saint-Mande, France

    Yizi Chen, Clément Mallet & Julien Perret

  3. LaDéHiS, CRH, EHESS, Paris, France

    Bertrand Duménieu & Julien Perret

Authors
  1. Yizi Chen
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  2. Edwin Carlinet
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  3. Joseph Chazalon
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  4. Clément Mallet
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  5. Bertrand Duménieu
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  6. Julien Perret
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Correspondence to Yizi Chen .

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Editors and Affiliations

  1. Uppsala University, Uppsala, Sweden

    Joakim Lindblad

  2. Uppsala University, Uppsala, Sweden

    Filip Malmberg

  3. Uppsala University, Uppsala, Sweden

    Nataša Sladoje

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Chen, Y., Carlinet, E., Chazalon, J., Mallet, C., Duménieu, B., Perret, J. (2021). Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation. In: Lindblad, J., Malmberg, F., Sladoje, N. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2021. Lecture Notes in Computer Science(), vol 12708. Springer, Cham. https://doi.org/10.1007/978-3-030-76657-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-76657-3_5

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  • Online ISBN: 978-3-030-76657-3

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