Segmentation of Chinese Postal Envelope Images for Address Block Location

  • Xinghui Dong
  • Junyu Dong
  • Shengke Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5876)


In this paper, we propose a simple segmentation approach for camera-captured Chinese envelope images. We first apply a moving-window thresholding algorithm, which is less curvature-biased and less sensitive to noise than other local thresholding methods, to generate binary images. Then the skew images are corrected by using a skew detection and correction algorithm. In the following stage rectangular frames on the envelopes containing postcode are removed by using opening operators in mathematical morphology. Finally, a post-processing procedure is used to remove remaining thin lines. In this stage, connected components are labeled. We test 800 camera-captured envelope images in our experiments, including handwritten and machine-printed envelopes. For almost all of these images, the proposed approach can accurately separate the address block, stamp and postmark from the background.


Binary Image Document Image Mathematical Morphology Sobel Operator Rectangular Frame 
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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  1. 1.
    Lu, Y., Tan, C.L., Shi, P., Zhang, K.: Segmentation of Handwritten Chinese Characters from Destination Addresses of Mail Pieces. International journal of pattern recognition and artificial intelligence 16, 85–96 (2002)CrossRefGoogle Scholar
  2. 2.
    Menoti, D., Borges, D.L., Facon, J., de Souza Britto Jr., A.: Segmentation of Postal Envelopes for Address Block Location: an approach based on feature selection in wavelet space. In: Proceedings of Seventh International Conference on Document Analysis and Recognition, pp. 699–703 (2003)Google Scholar
  3. 3.
    Yonekura, E.A., Facon, J.: Postal Envelope Segmentation by 2-D Histogram Clustering through Watershed Transform. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 1, pp. 338–342 (2003)Google Scholar
  4. 4.
    Menoti, D., Borges, D.L., et al.: Salient Features and Hypothesis Testing: evaluating a novel approach for segmentation and address block location. In: International Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, pp. 26–33 (2003)Google Scholar
  5. 5.
    Legal-Ayala, H.A., Facon, J., Barán, B.: Postal Envelope Segmentation using Learning-Based Approach. CLEI Electron. J. 11 (2008)Google Scholar
  6. 6.
    Wu, S., Amin, A.: Automatic thresholding of gray-level using multistage approach. In: Proceedings of Seventh International Conference on Document Analysis and Recognition, vol. 1, pp. 493–497 (2003)Google Scholar
  7. 7.
    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  8. 8.
    Kittler, J., Illingworth, J., Foglein, J.: Threshold selection based on a simple image statistic. Computer Vision, Graphics and Image Processing, 125–147 (1985)Google Scholar
  9. 9.
    Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice Hall, Englewood Cliffs (1986)Google Scholar
  10. 10.
    Sauvola, J., Pietikainen, M.: Adaptive Document Image Binarization. Pattern Recognition 33, 225–236 (2000)CrossRefGoogle Scholar
  11. 11.
    Yanowitz, S.D., Bruckstein, A.M.: A new method for image segmentation. In: Proceedings of 9th International Conference on Pattern Recognition, pp. 270–275 (1988)Google Scholar
  12. 12.
    Liang, J., Doermann, D., Li, H.: Camera-based analysis of text and documents: a survey. In: IJDAR, vol. 7, pp. 84–104 (2005)Google Scholar
  13. 13.
    Trier, O., Jain, A.: Goal Directed Evaluation of Binarization Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1191–1201 (1995)Google Scholar
  14. 14.
    Trier, O., Taxt, T.: Evaluation of binarization methods for document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 312–315 (1995)Google Scholar
  15. 15.
    Wilkinson, H.F.M., et al.: Blood vessel segmentation using moving-window robust automatic threshold selection. In: International Conference on Image Processing, Barcelona, vol. 2, pp. 1093–1096 (2003)Google Scholar
  16. 16.
    Gatos, B., Pratikakis, I., Perantonis, S.J.: An Adaptive Binarization Technique for Low Quality Historical Documents. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 102–113. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    Wilkinson, M.H.F.: Optimizing edge detectors for robust automatic threshold selection: coping with edge curvature and noise. In: GMIP, pp. 385–401 (1998)Google Scholar
  18. 18.
    Ye, X., Cheriet, M., Suen, C.Y., Liu, K.: Extraction of bankcheck items by mathematical morphology. In: IJDAR, vol. 2, pp. 53–66 (1999)Google Scholar
  19. 19.
    Yu, Z., Dong, J., Wei, Z., Shen, J.: A Fast Image Rotation Algorithm for Optical Character Recognition of Chinese Documents. In: Proceedings of the 4th International Conference on Communications, Circuits and Systems, pp. 485–489 (2006)Google Scholar
  20. 20.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)zbMATHGoogle Scholar
  21. 21.
    Said, J.N.: Automatic Processing of Documents and Bank Cheques. In: PhD thesis, Concordia University (1998)Google Scholar
  22. 22.
    Chang, F., Chen, C.-J.: A component-labeling algorithm using contour tracing technique. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 2, pp. 741–745 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xinghui Dong
    • 1
  • Junyu Dong
    • 1
  • Shengke Wang
    • 1
  1. 1.Department of Computer Science and TechnologyOcean University of ChinaQingdaoChina

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