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A New Technique for Global and Local Skew Correction in Binary Documents

  • Michael Makridis
  • Nikos Nikolaou
  • Nikos Papamarkos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)

Abstract

A new technique for global and local skew correction in binary documents is proposed. The proposed technique performs a connected component analysis and for each connected component, document’s local skew angle is estimated, based on detecting a sequence of other consecutive connected components, at certain directions, within a specified neighborhood. A histogram of all local skew angles is constructed. If the histogram has one peak then global skew correction is performed, otherwise the document has more than one skews. For local skew correction, a page layout analysis is performed based on a boundary growth algorithm at different directions. The exact global or local skew is approached with a least squares line fitting procedure. The accuracy of the technique has been tested using many documents of different skew and it is compared with two other similar techniques.

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References

  1. 1.
    Amin, A., Fischer, S.: A document skew detection method using the Hough transform. Pattern Analysis and Applications 3, 243–253 (2000)zbMATHCrossRefGoogle Scholar
  2. 2.
    Yin, P.Y.: Skew detection and block classification of printed documents. Image and Vision Computing 19, 567–579 (2001)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Leung, M.K.H., Hui, S.C.: Cursive word reference line detection. Pattern Recognition 30, 503–511 (1997)CrossRefGoogle Scholar
  4. 4.
    Kwag, H.K., Kim, S.H., Jeong, S.H., Lee, G.S.: Efficient skew estimation and correction algorithm for document images. Image and Vision Computing 20, 25–35 (2002)CrossRefGoogle Scholar
  5. 5.
    Postl, W.: Detection of linear oblique structure and skew in digitized documents. In: Proceedings 8th Int. Conf. on Pattern Recognition, pp. 464–468 (1986)Google Scholar
  6. 6.
    Gatos, B., Papamarkos, N., Chamzas, C.: Skew detection and text line position determination in digitized documents. Pattern Recognition 30, 1505–1519 (1997)CrossRefGoogle Scholar
  7. 7.
    Baird, H.S.: The skew angle of printed documents. In: O’Gorman, L., Kasturi, R. (eds.) The skew angle of printed documents, pp. 204–208. IEEE CS Press, Los Alamitos (1995)Google Scholar
  8. 8.
    Akiyama, T., Hagita, N.: Automated entry system for printed documents. Pattern Recognition 23, 1141–1154 (1990)CrossRefGoogle Scholar
  9. 9.
    Pavlidis, T., Zhou, J.: Page segmentation by white streams. In: Proceedings 1st Int. Conf. Document Analysis and Recognition, pp. 945–953 (1991)Google Scholar
  10. 10.
    Ciardiello, G., Scafuro, G., Degrandi, M.T., Spada, M.R., Roccotelli, M.P.: An experimental system for office document handling and text recognition. In: Proceedings 9th Int. Conf. on Pattern Recognition, Milano, pp. 739–743 (1988)Google Scholar
  11. 11.
    Kapoor, R., Bagai, D., Kamal, T.S.: A new algorithm for skew detection and correction. Pattern Recognition Letters 25, 1215–1229 (2004)CrossRefGoogle Scholar
  12. 12.
    Hashizume, A., Yeh, P.S., Rosenfeld, A.: A method of detecting the orientation of aligned components. Pattern Recognition 4, 125–132 (1986)CrossRefGoogle Scholar
  13. 13.
    Liu, J., Lee, C.M., Shu, R.B.: An efficient method for the skew normalization of a document image. In: Proceedings Int. Conf. on Pattern Recognition, vol. 3, pp. 122–125 (1992)Google Scholar
  14. 14.
    O’Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 15, 1162–1173 (1993)CrossRefGoogle Scholar
  15. 15.
    Yan, H.: Skew correction of document images using interline cross-correlation. Graphical Models and Image Processing 55, 538–543 (1993)CrossRefGoogle Scholar
  16. 16.
    Chaudhuri, A., Chaudhuri, S.: Robust detection of skew in document images. IEEE Transactions on Image Processing 6, 344–349 (1997)CrossRefGoogle Scholar
  17. 17.
    Chou, C.H., Chu, S.Y., Chang, F.: Estimation of Document Skew Angles Using Piecewise Linear Approximation of Line Objects. Pattern Recognition 40, 443–455 (2007)zbMATHCrossRefGoogle Scholar
  18. 18.
    Dhandra, B.V., Malemath, V.S., Mallikarjun, H., Hegadi, R.: Skew Detection in Binary Image Documents Based on Image Dilation and Region labeling Approach. In: Proceedings 18th Int. Conf. on Pattern Recognition, vol. 2, pp. 954–957 (2006)Google Scholar
  19. 19.
    Saragiotis, P., Papamarkos, N.: Skew correction in documents with several differently skewed text areas. In: Int. Conf. on Computer Vision Theory and Applications, Barcelona (2007)Google Scholar
  20. 20.
    Phillips, I.T.: User’s Reference manual for the UW English/Technical Document Image Database I. UW-I English/Technical Document Image Database, University of Washington (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Makridis
    • 1
  • Nikos Nikolaou
    • 1
  • Nikos Papamarkos
    • 1
  1. 1.Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, 67100 XanthiGreece

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