Two Thresholding for Deriving the Bi-level Document Image

  • Yu-Kumg Chen
  • Yi-Fan Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Optical character recognition occupies a very important field in digital image processing. It is used extensively in daily life. If the given image does not have a bimodal intensity histogram, it will cause segmenting mistake easily for the previous algorithms of image binarization. In order to solve this problem, a new algorithm is proposed in this paper. The proposed algorithm uses the theory of moving average on the histogram of the fuzzy image, and then derives the better histogram. Since use only one thresholding value cannot solve this problem completely, the edge information and the window processing are introduced in this paper for advanced thresholding. Thus, a more refine bi-level image is derived and it will result in the improvement of optical character recognition. Experiments are carried out for some samples with shading to demonstrate the computational advantage of the proposed method.


Original Image Document Image Optical Character Recognition Thresholding Method Edge Information 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41, 233–260 (1988)CrossRefGoogle Scholar
  2. 2.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on systems, Man, and Cybernetics 17, 1191–1201 (1979)Google Scholar
  3. 3.
    Trier, O.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Transactions on Pattern Analysis And Machine Intelligence 17, 1191–1201 (1995)CrossRefGoogle Scholar
  4. 4.
    Niblack, W.: An Introduction to Digital Image Processing. Pretice-Hall, Englewood Cliffs (1986)Google Scholar
  5. 5.
    Liu, Y., Srihari, S.N.: Document image binarization based on texture features. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 533–540 (1997)Google Scholar
  6. 6.
    Zhao, M., Yang, Y., Yan, H.: An adaptive thresholding method for binarization of blueprint images. Pattern Recognition Letter 21, 927–943 (2000)CrossRefGoogle Scholar
  7. 7.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13, 146–165 (2004)CrossRefGoogle Scholar
  8. 8.
    Jones, C.P.: Investments Analysis and Management. Wiley, Chichester (1991)Google Scholar
  9. 9.
    Sobel, I.E.: Camera Models and Machine Perception. PhD thesis, Stanford University (1970)Google Scholar
  10. 10.
    Bersen, J.: Dynamic thresholding of gray-level images. In: Proceeding Eighth International Conference Pattern Recognition, pp. 1251–1255 (1986)Google Scholar
  11. 11.
    Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognition 33, 225–236 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu-Kumg Chen
    • 1
  • Yi-Fan Chang
    • 1
  1. 1.Department of Electronic EngineeringHuafan UniversityTaipeiTaiwan

Personalised recommendations