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Historical Printed Ornaments: Dataset and Tasks

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Document Analysis and Recognition - ICDAR 2024 (ICDAR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14806))

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Abstract

This paper aims to develop the study of historical printed ornaments with modern unsupervised computer vision. We highlight three complex tasks that are of critical interest to book historians: clustering, element discovery, and unsupervised change localization. For each of these tasks, we introduce an evaluation benchmark, and we adapt and evaluate state-of-the-art models. Our Rey’s Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in. It focuses on an XVIIIth century bookseller, Marc-Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. Our results highlight the limitations of state-of-the-art models when faced with real data and show simple baselines such as k-means or congealing can outperform more sophisticated approaches on such data. Our dataset and code can be found at https://printed-ornaments.github.io/.

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Acknowledgement

This work was funded by ANR ROIi project ANR-20-CE38-0005. S. Baltaci, E. Vincent, and M. Aubry were supported by ERC project DISCOVER funded by the European Union’s Horizon Europe Research and Innovation program under grant agreement No. 101076028 and ANR VHS project ANR-21-CE38-0008. We thank Silya Ounoughi, Thomas Gautrais, and Vincent Ventresque for their work in the collection and annotation of the datasets, and Ségolène Albouy, Raphaël Baena, Syrine Kalleli, Ioannis Siglidis, Gurjeet Sangra Singh, Andrea Morales Garzón and Malamatenia Vlachou for valuable feedbacks.

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Authors and Affiliations

  1. Université de Lyon, UJM-St-Etienne, CNRS, Institut d’Optique Graduate School, Inria, Laboratoire Hubert Curien UMR 5516, 42023, Saint-Etienne, France

    Sayan Kumar Chaki, Remi Emonet & Thierry Fournel

  2. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, France

    Zeynep Sonat Baltaci, Elliot Vincent & Mathieu Aubry

  3. Inria, Ecole Normale Supérieure, CNRS, PSL Research University, Paris, France

    Elliot Vincent

  4. IUF University Institute of France, Paris, France

    Remi Emonet

  5. UJM-St-Etienne, CNRS, IHRIM 5317, Saint-Etienne, France

    Fabienne Vial-Bonacci & Christelle Bahier-Porte

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  1. Sayan Kumar Chaki
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Correspondence to Sayan Kumar Chaki or Zeynep Sonat Baltaci .

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  1. Luleå Tekniska Universitet, Luleå, Sweden

    Elisa H. Barney Smith

  2. Luleå Tekniska Universitet, Luleå, Sweden

    Marcus Liwicki

  3. Tsinghua University, Beijing, China

    Liangrui Peng

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Chaki, S.K. et al. (2024). Historical Printed Ornaments: Dataset and Tasks. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14806. Springer, Cham. https://doi.org/10.1007/978-3-031-70543-4_15

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