Page Frame Detection for Marginal Noise Removal from Scanned Documents

  • Faisal Shafait
  • Joost van Beusekom
  • Daniel Keysers
  • Thomas M. Breuel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


We describe and evaluate a method to robustly detect the page frame in document images, locating the actual page contents area and removing textual and non-textual noise along the page borders. We use a geometric matching algorithm to find the optimal page frame, which has the advantages of not assuming the existence of whitespace between noisy borders and actual page contents, and of giving a practical solution to the page frame detection problem without the need for parameter tuning. We define suitable performance measures and evaluate the algorithm on the UW-III database. The results show that the error rates are below 4% for each of the performance measures used. In addition, we demonstrate that the use of page frame detection reduces the optical character recognition (OCR) error rate by removing textual noise. Experiments using a commercial OCR system show that the error rate due to elements outside the page frame is reduced from 4.3% to 1.7% on the UW-III dataset.


  1. 1.
    Shafait, F., Keysers, D., Breuel, T.M.: Pixel-accurate representation and evaluation of page segmentation in document images. In: 18th Int. Conf. on Pattern Recognition, Hong Kong, China, Aug. 2006, pp. 872–875 (2006)Google Scholar
  2. 2.
    Baird, H.S.: Background structure in document images. In: Bunke, H., et al. (eds.) Document Image Analysis, pp. 17–34. World Scientific, Singapore (1994)Google Scholar
  3. 3.
    Breuel, T.M.: Two Geometric Algorithms for Layout Analysis. In: Lopresti, D.P., Hu, J., Kashi, R.S. (eds.) DAS 2002. LNCS, vol. 2423, pp. 188–199. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    O’Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(11), 1162–1173 (1993)CrossRefGoogle Scholar
  5. 5.
    Kise, K., Sato, A., Iwata, M.: Segmentation of page images using the area Voronoi diagram. Computer Vision and Image Understanding 70(3), 370–382 (1998)CrossRefGoogle Scholar
  6. 6.
    Le, D.X., Thoma, G.R., Wechsler, H.: Automated borders detection and adaptive segmentation for binary document images. In: 13th Int. Conf. Patt. Recog., Vienna, Austria, Aug. 1996, pp. 737–741 (1996)Google Scholar
  7. 7.
    Avila, B.T., Lins, R.D.: Efficient removal of noisy borders from monochromatic documents. In: Int. Conf. on Image Analysis and Recognition, Porto, Portugal, Sep. 2004, pp. 249–256 (2004)Google Scholar
  8. 8.
    Fan, K.C., Wang, Y.K., Lay, T.R.: Marginal noise removal of document images. Patt. Recog. 35, 2593–2611 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Cinque, L., Levialdi, S., Lombardi, L., Tanimoto, S.: Segmentation of page images having artifacts of photocopying and scanning. Patt. Recog. 35, 1167–1177 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Shafait, F., Keysers, D., Breuel, T.M.: Performance comparison of six algorithms for page segmentation. In: 7th IAPR Workshop on Document Analysis Systems, Nelson, New Zealand, Feb. 2006, pp. 368–379 (2006)Google Scholar
  11. 11.
    Breuel, T.M.: A practical, globally optimal algorithm for geometric matching under uncertainty. Electr. Notes Theor. Comput. Sci. 46, 1–15 (2001)CrossRefGoogle Scholar
  12. 12.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)MathSciNetGoogle Scholar
  13. 13.
    Phillips, I.T.: User’s reference manual for the UW english/technical document image database III. Technical report, Seattle University, Washington (1996)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Faisal Shafait
    • 1
  • Joost van Beusekom
    • 2
  • Daniel Keysers
    • 1
  • Thomas M. Breuel
    • 2
  1. 1.Image Understanding and Pattern Recognition (IUPR) research group, German Research Center for Artificial Intelligence (DFKI) GmbH, D-67663 KaiserslauternGermany
  2. 2.Department of Computer Science, Technical University of Kaiserslautern, D-67663 KaiserslauternGermany

Personalised recommendations