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Automatic Perspective Correction of Manuscript Images

  • Ryan Baumann
  • Christopher Blackwell
  • W. Brent Seales
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7634)

Abstract

Frequently, images of rare documents must be taken under strict time constraints, when a chance opportunity arises, and with equipment that is less than ideally suited for precise digitization. This can often result in uncalibrated images whose contents are nonetheless qualitatively useful. Due to the logistics of document imaging, perspective distortion is a common artifact which can manifest itself in images taken under these constrained circumstances. We propose an automated approach for correcting this perspective distortion, even for uncalibrated images of documents with irregular page edges containing no regular text.

Keywords

Document Image Canny Edge Edge Line Pattern Recognition Letter Manuscript Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Clark, P., Mirmehdi, M.: Estimating the orientation and recovery of text planes in a single image. In: Proceedings of the 12th British Machine Vision Conference, pp. 421–430 (2001)Google Scholar
  2. 2.
    Clark, P., Mirmehdi, M.: On the recovery of oriented documents from single images. In: Proceedings of ACIVS, pp. 190–197 (2002)Google Scholar
  3. 3.
    Clark, P., Mirmehdi, M.: Rectifying perspective views of text in 3D scenes using vanishing points. Pattern Recognition 36(11), 2673–2686 (2003)CrossRefGoogle Scholar
  4. 4.
    Cao, Y., Wang, S., Li, H.: Skew detection and correction in document images based on straight-line fitting. Pattern Recognition Letters 24(12), 1871–1879 (2003)CrossRefGoogle Scholar
  5. 5.
    Lu, Y., Lim Tan, C.: A nearest-neighbor chain based approach to skew estimation in document images. Pattern Recognition Letters 24(14), 2315–2323 (2003)CrossRefGoogle Scholar
  6. 6.
    Zhang, L., Tan, C.: Warped image restoration with applications to digital libraries. In: Proceedings of Eighth International Conference on Document Analysis and Recognition, vol. 1, pp. 192–196 (2005)Google Scholar
  7. 7.
    Monnier, C., Ablavsky, V., Holden, S., Snorrason, M.: Sequential correction of perspective warp in camera-based documents. In: Proceedings of Eighth International Conference on Document Analysis and Recognition, pp. 394–398 (2005)Google Scholar
  8. 8.
    Liang, J., DeMenthon, D., Doermann, D.: Flattening curved documents in images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 338–345 (2005)Google Scholar
  9. 9.
    Ulges, A., Lampert, C.H., Breuel, T.: Document image dewarping using robust estimation of curled text lines. In: Proceedings of Eighth International Conference on Document Analysis and Recognition, vol. 2, pp. 1001–1005 (2005)Google Scholar
  10. 10.
    Ezaki, H., Uchida, S., Asano, A., Sakoe, H.: Dewarping of document image by global optimization. In: Proceedings of Eighth International Conference on Document Analysis and Recognition, pp. 302–306 (2005)Google Scholar
  11. 11.
    Pollard, S., Pilu, M.: Building cameras for capturing documents. International Journal on Document Analysis and Recognition 7(2), 123–137 (2005)CrossRefGoogle Scholar
  12. 12.
    Lu, S., Chen, B.M., Ko, C.C.: Perspective rectification of document images using fuzzy set and morphological operations. Image and Vision Computing 23(5), 541–553 (2005)CrossRefGoogle Scholar
  13. 13.
    Ávila, B.T., Lins, R.D.: A fast orientation and skew detection algorithm for monochromatic document images. In: DocEng 2005: Proceedings of the 2005 ACM Symposium on Document Engineering, ACM Request Permissions (November 2005)Google Scholar
  14. 14.
    Zhang, W., Li, X., Ma, X.: Perspective Correction Method for Chinese Document Images. In: International Symposium on Intelligent Information Technology Application Workshops, IITAW 2008, pp. 467–470 (2008)Google Scholar
  15. 15.
    Liang, J., DeMenthon, D., Doermann, D.: Geometric Rectification of Camera-Captured Document Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(4), 591–605 (2008)CrossRefGoogle Scholar
  16. 16.
    Bukhari, S., Shafait, F.: Dewarping of document images using coupled-snakes. In: Proc. of 3rd Int. Workshop on … (2009)Google Scholar
  17. 17.
    Beusekom, J., Shafait, F., Breuel, T.M.: Combined orientation and skew detection using geometric text-line modeling. International Journal of Document Analysis and Recognition (IJDAR) 13(2), 79–92 (2010)CrossRefGoogle Scholar
  18. 18.
    Luo, S., Fang, X., Zhao, C., Luo, Y.: Text Line Based Correction of Distorted Document Images. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1494–1499 (2011)Google Scholar
  19. 19.
    Rahnemoonfar, M., Antonacopoulos, A.: Restoration of Arbitrarily Warped Historical Document Images Using Flow Lines. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 905–909 (2011)Google Scholar
  20. 20.
    Golpardaz, M., Nezamabadi-Pour, H.: Perspective Rectification and Skew Correction in Camera-Based Farsi Document Images. In: 2011 7th Iranian on Machine Vision and Image Processing (MVIP), pp. 1–5 (2011)Google Scholar
  21. 21.
    Yang, P., Antonacopoulos, A., Clausner, C., Pletschacher, S.: Grid-based modelling and correction of arbitrarily warped historical document images for large-scale digitisation. In: HIP 2011: Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, ACM Request Permissions (September 2011)Google Scholar
  22. 22.
    Pilu, M.: Undoing paper curl distortion using applicable surfaces. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-67–I-72 (2001)Google Scholar
  23. 23.
    Brown, M., Seales, W.: Document restoration using 3D shape. In: Proceedings of ICCV 2001 (2001)Google Scholar
  24. 24.
    Brown, M., Seales, W.: Image restoration of arbitrarily warped documents. In: Pattern Analysis and Machine Intelligence (2004)Google Scholar
  25. 25.
    Brown, M., Pisula, C.: Conformal deskewing of non-planar documents. In: Computer Vision and Pattern Recognition (2005)Google Scholar
  26. 26.
    Brown, M., Sun, M., Yang, R., Yun, L., Seales, W.: Restoring 2D Content from Distorted Documents. In: Pattern Analysis and Machine Intelligence (2007)Google Scholar
  27. 27.
    Zhang, L., Zhang, Y., Tan, C.L.: An Improved Physically-Based Method for Geometric Restoration of Distorted Document Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(4), 728–734 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryan Baumann
    • 1
  • Christopher Blackwell
    • 2
  • W. Brent Seales
    • 1
  1. 1.University of KentuckyUSA
  2. 2.Furman UniversityUSA

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