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PDTW150K: A Dataset for Patent Drawing Retrieval

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14565))

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Abstract

We introduce a new large-scale patent dataset termed PDTW150K for patent drawing retrieval. The dataset contains more than 150,000 patents associated with text metadata and over 850,000 patent drawings. We also provide a set of bounding box positions of individual drawing views to support constructing object detection models. We design some experiments to demonstrate the possible ways of using PDTW150K, including image retrieval, cross-modal retrieval, and object detection tasks. PDTW150K is available for download on GitHub [1].

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Notes

  1. 1.

    The other categories are invention pattern and utility model pattern.

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Acknowledgments

This work was supported by the National Science and Technology Council under grants NSTC 112-2221-E-415-008-MY3.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chiayi University, Chiayi, Taiwan

    Chan-Ming Hsu, Tse-Hung Lin, Yu-Hsien Chen & Chih-Yi Chiu

Authors
  1. Chan-Ming Hsu
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  2. Tse-Hung Lin
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  3. Yu-Hsien Chen
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  4. Chih-Yi Chiu
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Corresponding author

Correspondence to Chih-Yi Chiu .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Bei**g, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Hsu, CM., Lin, TH., Chen, YH., Chiu, CY. (2024). PDTW150K: A Dataset for Patent Drawing Retrieval. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14565. Springer, Cham. https://doi.org/10.1007/978-3-031-56435-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-56435-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56434-5

  • Online ISBN: 978-3-031-56435-2

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