An Efficient Image Registration Method based on Modified NonLocal-Means - Application to Color Business Document Images

Louisa Kessi, Frank Lebourgeois, Christophe Garcia

2015

Abstract

Most of business documents, in particular invoices, are composed of an existing color template and an added filled-in text by the users. The direct layout analysis without separating the preprinted form from the added text is difficult and not efficient. Previous works use both local features and global layout knowledge to separate the pre-printed forms and the added text. Although for real applications, they are even exposed to a great improvement. This paper presents the first pixel-based image registration of color business documents based on the NonLocal-Means (NLM) method. We prove that the NLM, commonly used for image denoising, can be also adapted to images registration at the pixel level. Our intuition tends to look for a similar neighbourhood from the first image I1 into the second image I2 and provide both an exact image registration with a precision at pixel level and noise removal. We show the feasibility of this approach on several color images of various invoices and forms in real situation and its application to the layout analysis. Applied on color documents, the proposed algorithm shows the benefits of the NLM in this context.

References

  1. Y.Y. Tang, C.Y. Suen, C.D. Yan, and M. Cheriet, “Financial Document Processing Based on Staff Line and Description Language,” IEEE Trans. Systems, Man, and Cybernetics, vol. 25, no. 5, pp. 738-753, 1995.
  2. R. Casey, D. Ferguson, K. Mohiuddin, and E. Walach, “Intelligent Forms Processing System,” Machine Vision and Applications, vol. 5, no. 5, pp. 143-155, 1992.
  3. S.L. Taylor, R. Fritzson, and J.A. Pastor, “Extraction of Data From Preprinted Forms,” Machine Vision and Applications, vol. 5, no. 5, pp. 211-222, 1992.
  4. T.M. Ha and H. Bunke, “Model-Based Analysis and Understanding of Check Forms,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 8, no. 5, pp. 1,053-1,081, 1994.
  5. H. Peng, F. Long etc. "Document Image Recognition Based on Template Matching of Component Block Projections", IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(9):1188-1192.
  6. Z. Yang, S. Cohen. "Image registration and object recognition using affine invariants and convex hulls", IEEE Trans Image Process. 1999;8(7):934-46. doi: 10.1109/83.772236.
  7. D. H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, 13, No. 2, 1981, pp111-122.
  8. Aitken CL, et al. "Tumor localization and image registration of F-18 FDG coincidence detection scans with computed tomographic scans". Clin Nucl Med. 2002 Apr; 27(4):275-82.
  9. A. Rastogi and S. N. Srihari, "Recognizing textual blocks in document images using the Hough transform," TR 86-01, Dept. of Computer Science, SUNY Buffalo, NY, Jan 1986.
  10. Garris, M.D., Grother, P.J, "Generalized form registration using structure-based techniques", Proceedings of the Fifth Annual Symposium on Document Analysis and Information Retrieval, pp.321-334 (1996).
  11. S. C. Hinds, J. L. Fisher and D. P. D'Amato, "A Document Skew Detection Method Using Run-Length Encoding and the Hough Transform," 10th International Conference on Pattern Recognition, vol. 1, pp.464- 468, 1990.
  12. F. Cesarini, M. Gori, S. Marinai, and G. Soda, “INFORMys: A Flexible Invoice-Like Form-Reader System,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 710-745, July 1998.
  13. D.P. Lopresti, “String Techniques for Detecting Duplicates in Document Databases,” Int'l J. Document Analysis and Recognition, vol. 2, no. 4, pp. 186-199, 2000.
  14. L., Tseng, and R., Chen, "The recognition of form documents based on three types of line segments," Proc of 4th Int Conf on Document Analysis and Recognition, 1, pp.71-75, 1997.
  15. K., Fan, and M., Chang, "Form document identification using line structure based features," Proc of 14th Int Conf on Pattern Recognition, 2, pp.1098-1100, 1998.
  16. T., Watanabe, and X., Huang, "Automatic acquisition of layout knowledge for understanding business cards," Proc of 4th Int Conf on Document Analysis and Recognition, 1, pp.216-220, 1997.
  17. R. Safari, N.N.et al, "Document registration using projective geometry," IEEE Trans on Image Processing, 6(9), pp.1337-1341, 1997.
  18. H. Peng, F. Long, Z. Chi, D. Feng, and W. Siu, “Document Image Matching Based on Component Blocks,” Proc. Int'l Conf. Image Processing, pp. 601- 604, Sept. 2000.
  19. J. Hu, R. Kashi, and G.Wilfong, “Document Image Layout Comparison and Classification,” Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 285-288, 1999.
  20. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms with a new one,” Multiscale Model. Simul., vol. 4, pp. 490-530, 2005.
  21. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. IEEE Int.Conf. Computer Vision, Jan. 1998, pp. 836-846.
  22. P. Coupé , P. Yger, S. Prima , P. Hellier, C. Kervrann, and C. Barillot "An Optimized Block wise Non Local Means Denoising Filter for 3D Magnetic Resonance Images", Transactions on Medical Imaging, (2007) 18.
  23. A. Buades, B. Coll, and J. M. Morel, “Denoising image sequences does not require motion estimation,” in Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, Sep. 2005, pp. 70-74.
  24. Manjunath Aradhya V N, et al.“Skew detection technique for binary document images based on Hough transform”, International Journal of Information Technology, Vol. 3,
  25. Shivakumara P. et al., “Skew detection in Binary document image using Linear Regression Analysis”, proc. Of National Conf. on Advanced Computer Application NCAC-2002, Pollachi, India 2002, pp 41- 46.
  26. Najman L., “Using mathematical morphology for document skew estimation”, SPIE Document Recognition and retrievals XI vol. 5296, 2004, pp 182- 191.
  27. Kessi L., Le Bourgeois F.,Garcia C., Duong J. “AColDPS : Robust and Unsupervised Automatic Color Document Processing System”, VISAPP'15.
Download


Paper Citation


in Harvard Style

Kessi L., Lebourgeois F. and Garcia C. (2015). An Efficient Image Registration Method based on Modified NonLocal-Means - Application to Color Business Document Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 166-173. DOI: 10.5220/0005315301660173


in Bibtex Style

@conference{visapp15,
author={Louisa Kessi and Frank Lebourgeois and Christophe Garcia},
title={An Efficient Image Registration Method based on Modified NonLocal-Means - Application to Color Business Document Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={166-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005315301660173},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - An Efficient Image Registration Method based on Modified NonLocal-Means - Application to Color Business Document Images
SN - 978-989-758-089-5
AU - Kessi L.
AU - Lebourgeois F.
AU - Garcia C.
PY - 2015
SP - 166
EP - 173
DO - 10.5220/0005315301660173