skip to main content
10.1145/2064975.2064979acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Patent image retrieval: a survey

Published:24 October 2011Publication History

ABSTRACT

Drawings are an important component of patents, and many search tasks in the intellectual property domain rely on the comparison of patent drawings. In this paper, we begin with a review of algorithms developed for the automated retrieval of similar images in the patent domain. There is however a larger body of research dedicated to analysis and retrieval of images found in technical documents that is also applicable to the images found in patents. We present an overview of this research for technical drawings, diagrams, charts, plots and chemical structures. Finally, we discuss the evaluation of image retrieval for patents, including short descriptions of the new patent image evaluation tasks in the CLEF-IP and TREC-CHEM evaluation campaigns in 2011.

References

  1. S. Adams. Electronic non-text material in patent applications - some questions for patent offices, applicants and searchers. World Patent Information, 27(2):99--103, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  2. A. Antonacopoulos and D. Kennedy. Information extraction from complex circular charts. Document Analysis and Recognition, International Conference on, 0:784--787, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. V. Bakergem. Image collections in the design studio. The electronic design studio, pages 261--271, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Barta and I. Vajk. Document image analysis by probabilistic network and circuit diagram extraction. Informatica, An International Journal of Computing and Informatics, 29:291--301, 2005.Google ScholarGoogle Scholar
  5. D. Blostein, E. Lank, and R. Zanibbi. Treatment of diagrams in document image analysis. In Proceedings of the First International Conference on Theory and Application of Diagrams, pages 330--344, London, UK, 2000. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. W. Browuer, S. Kataria, S. Das, P. Mitra, and C. L. Giles. Segregating and extracting overlapping data points in two-dimensional plots. In JCDL, pages 276--279, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W. H. Clayton M. Enhancing the sketchbook. Proceedings of the Association for Computer Aided Design in Architecture (ACADIA 91), 1991.Google ScholarGoogle Scholar
  8. M. Das, R. Manmatha, and E. M. Riseman. Indexing flower patent images using domain knowledge. IEEE Intelligent Systems, 14(5):24--33, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):1--60, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. Feng, C. Viard-Gaudin, and Z. Sun. On-line hand-drawn electric circuit diagram recognition using 2d dynamic programming. Pattern Recognition, 42(12):3215--3223, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. I. V. Filippov and M. C. Nicklaus. Optical structure recognition software to recover chemical information: Osra, an open source solution. Journal of Chemical Information and Modeling, 49(3):740--743, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  12. L. A. Fletcher and R. Kasturi. A robust algorithm for text string separation from mixed text/graphics images. IEEE Trans. Pattern Anal. Mach. Intell., 10(6):910--918, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W. Huang, C. L. Tan, and W. K. Leow. Model-based chart image recognition. In GREC, pages 87--99, 2003.Google ScholarGoogle Scholar
  14. B. Huet, G. Guarascio, N. J. Kern, and B. Mérialdo. Relational skeletons for retrieval in patent drawings. In ICIP (2), pages 737--740, 2001.Google ScholarGoogle Scholar
  15. S. Kataria, W. Browuer, P. Mitra, and C. L. Giles. Automatic extraction of data points and text blocks from 2-dimensional plots in digital documents. In AAAI, pages 1169--1174, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. List. How drawings could enhance retrieval in mechanical and device patent searching. World Patent Information, 29(3):210--218, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  17. X. Lu, S. Kataria, W. J. Brouwer, J. Z. Wang, P. Mitra, and C. L. Giles. Automated analysis of images in documents for intelligent document search. Int. J. Doc. Anal. Recognit.(IJDAR), 12(2):65--81, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. X. Lu, P. Mitra, J. Z. Wang, and C. L. Giles. Automatic categorization of figures in scientific documents. In JCDL, pages 129--138. ACM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Lupu, J. Huang, J. Zhu, and J. Tait. TREC Chemical Information Retrieval - An Evaluation Effort for Chemical IR Systems. World Patent Information, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  20. L. Najman, O. Gibot, and S. Berche. Indexing technical drawings using title block structure recognition. In Proc. International Conference on Document Analysis and Recognition (ICDAR), pages 587--591, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. R. Osborn. Theory pictures as trails: diagrams and the navigation of theoretical narratives. Cognitive Science Online, 3.2:14--44, 2005.Google ScholarGoogle Scholar
  22. P. Sidiropoulos, S. Vrochidis, and I. Kompatsiaris. Content-based binary image retrieval using the adaptive hierarchical density histogram. Pattern Recognition, 44(4):739--750, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22(12):1349--1380, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Tiwari and V. Bansal. Patseek: Content based image retrieval system for patent database. In ICEB, pages 1167--1171, 2004.Google ScholarGoogle Scholar
  25. K. Tombre. Analysis of engineering drawings: State of the art and challenges. In GREC, pages 257--264, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S. Vrochidis, S. Papadopoulos, A. Moumtzidou, P. Sidiropoulos, E. Pianta, and I. Kompatsiaris. Towards content-based patent image retrieval: A framework perspective. World Patent Information, 32(2):94--106, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  27. N. Weber and A. Henrich. Retrieval of technical drawings in dxf format - concepts and problems. In LWA, pages 213--220, 2007.Google ScholarGoogle Scholar
  28. Z. Zhiyuan, Z. Juan, and X. Bin. An outward-appearance patent-image retrieval approach based on the contour-description matrix. In FCST, pages 86--89, Washington, DC, USA, 2007. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y. Zhou and C. L. Tan. Chart analysis and recognition in document images. In ICDAR, page 1055, Washington, DC, USA, 2001. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Y. P. Zhou and C. L. Tan. Hough technique for bar charts detection and recognition in document images. In International Conference on Image Processing, ICIP 2000, pages 494--497, 2000.Google ScholarGoogle Scholar

Index Terms

  1. Patent image retrieval: a survey

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        PaIR '11: Proceedings of the 4th workshop on Patent information retrieval
        October 2011
        46 pages
        ISBN:9781450309554
        DOI:10.1145/2064975

        Copyright © 2011 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 October 2011

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate7of13submissions,54%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader