DeepPatent: Large Scale Patent Drawing Recognition and Retrieval

Document Type

Conference Paper

Publication Date

2022

DOI

10.1109/WACV51458.2022.00063

Publication Title

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Pages

557-566

Conference Name

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Jan 3-8, 2022, Waikoloa, HI

Abstract

We tackle the problem of analyzing and retrieving technical drawings. First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings. The dataset provides more than 350,000 design patent drawings for the purpose of image retrieval. Unlike existing datasets, DeepPatent provides fine-grained image retrieval associations within the collection of drawings and does not rely on cross-domain associations for supervision. We develop a baseline deep learning models, named PatentNet, based on best practices for training retrieval models for static images. We demonstrate the superior performance of PatentNet when trained on our fine-grained associations of DeepPatent against other deep learning approaches and classic computer vision descriptors, such as histogram of oriented gradients (HOG), on DeepPatent. With the introduction of this new dataset, and benchmark algorithms, we demonstrate that the analysis and retrieval of line drawings remains an open challenge in computer vision; and that patent drawing retrieval provides a concrete testbench to spur research.

Comments

These WACV 2022 papers are the Open Access versions, provided by the Computer Vision Foundation. The final published version of the proceedings is available on IEEE Xplore at: http://dx.doi.org/10.1109/WACV51458.2022.00063

Original Publication Citation

Kucer, M., Oyen, D., Castorena, J., & and Wu, J. (2022) DeepPatent: Large scale patent drawing recognition and retrieval. Paper presented at the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, January 3-8, 2022

ORCID

0000-0003-0173-4463 (Wu)

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