Document Type

Article

Publication Date

2023

DOI

10.1038/s41597-023-02653-7

Publication Title

Scientific Data

Volume

10

Issue

1

Pages

772 (1-14)

Abstract

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval.

Rights

© 2023 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "The code used for preprocessing and segmenting figures is publicly available on GitHub: https://github.com/lamps-lab/Patent-figure-segmentor and https://github.com/GoFigure-LANL/figure-segmentation. Similarly, the sofware used for extracting semantic information, including object names and viewpoints from patent captions, is publicly available at https://github.com/lamps-lab/Visual-Descriptor."

Original Publication Citation

Ajayi, K., Wei, X., Gryder, M., Shields, W., Wu, J., Jones, S. M., Kucer, M., & Oyen, D. (2023). DeepPatent2: A large-scale benchmarking corpus for technical drawing understanding. Scientific Data, 10(1), 1-14, Article 772. https://doi.org/10.1038/s41597-023-02653-7

ORCID

0000-0002-5124-0739 (Ajayi), 0000-0003-0173-4463 (Wu)

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