Skip to main content

Binarization of Degraded Document Images with Generalized Gaussian Distribution

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11540)


One of the most crucial steps of preprocessing of document images subjected to further text recognition is their binarization, which influences significantly obtained OCR results. Since for degrades images, particularly historical documents, classical global and local thresholding methods may be inappropriate, a challenging task of their binarization is still up-to-date. In the paper a novel approach to the use of Generalized Gaussian Distribution for this purpose is presented. Assuming the presence of distortions, which may be modelled using the Gaussian noise distribution, in historical document images, a significant similarity of their histograms to those obtained for binary images corrupted by Gaussian noise may be observed. Therefore, extracting the parameters of Generalized Gaussian Distribution, distortions may be modelled and removed, enhancing the quality of input data for further thresholding and text recognition. Due to relatively long processing time, its shortening using the Monte Carlo method is proposed as well. The presented algorithm has been verified using well-known DIBCO datasets leading to very promising binarization results.


  • Document images
  • Image binarization
  • Generalized Gaussian Distribution
  • Monte Carlo method
  • Thresholding

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-22750-0_14
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-22750-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.


  1. Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007).

    CrossRef  Google Scholar 

  2. Clarke, R.J.: Transform Coding of Images. Academic Press, New York (1985)

    Google Scholar 

  3. Feng, M.L., Tan, Y.P.: Adaptive binarization method for document image analysis. In: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME), vol. 1, pp. 339–342 (2004).

  4. Krupiński, R.: Approximated fast estimator for the shape parameter of generalized Gaussian distribution for a small sample size. Bull. Polish Acad. Sci. Tech. Sci. 63(2), 405–411 (2015).

    MathSciNet  CrossRef  Google Scholar 

  5. Krupiński, R.: Reconstructed quantized coefficients modeled with generalized Gaussian distribution with exponent 1/3. Image Process. Commun. 21(4), 5–12 (2016)

    CrossRef  Google Scholar 

  6. Krupiński, R.: Modeling quantized coefficients with generalized Gaussian distribution with exponent 1/m, \(m=2,3,\ldots \). In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds.) ICMMI 2017. AISC, vol. 659, pp. 228–237. Springer, Cham (2018).

    CrossRef  Google Scholar 

  7. Krupiński, R.: Generating augmented quaternion random variable with Generalized Gaussian Distribution. IEEE Access 6, 34608–34615 (2018).

    CrossRef  Google Scholar 

  8. Lavu, S., Choi, H., Baraniuk, R.: Estimation-quantization geometry coding using normal meshes. In: Proceedings of the Data Compression Conference (DCC 2003), p. 362, March 2003.

  9. Lech, P., Okarma, K.: Optimization of the fast image binarization method based on the Monte Carlo approach. Elektronika Ir Elektrotechnika 20(4), 63–66 (2014).

    CrossRef  Google Scholar 

  10. Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004).

    CrossRef  Google Scholar 

  11. Mitianoudis, N., Papamarkos, N.: Document image binarization using local features and Gaussian mixture modeling. Image Vis. Comput. 38, 33–51 (2015).

    CrossRef  Google Scholar 

  12. Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)

    Google Scholar 

  13. Novey, M., Adali, T., Roy, A.: A complex Generalized Gaussian Distribution - characterization, generation, and estimation. IEEE Trans. Signal Process. 58(3), 1427–1433 (2010).

    MathSciNet  CrossRef  MATH  Google Scholar 

  14. Ntirogiannis, K., Gatos, B., Pratikakis, I.: Performance evaluation methodology for historical document image binarization. IEEE Trans. Image Process. 22(2), 595–609 (2013).

    MathSciNet  CrossRef  MATH  Google Scholar 

  15. Okarma, K., Lech, P.: Monte Carlo based algorithm for fast preliminary video analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008).

    CrossRef  Google Scholar 

  16. Okarma, K., Lech, P.: Fast statistical image binarization of colour images for the recognition of the QR codes. Elektronika Ir Elektrotechnika 21(3), 58–61 (2015).

    CrossRef  Google Scholar 

  17. Olver, F.W.J.: Asymptotics and Special Functions. Academic Press, New York (1974)

    MATH  Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).

    CrossRef  Google Scholar 

  19. Pascal, F., Bombrun, L., Tourneret, J.Y., Berthoumieu, Y.: Parameter estimation for multivariate Generalized Gaussian Distributions. IEEE Trans. Signal Process. 61(23), 5960–5971 (2013).

    MathSciNet  CrossRef  MATH  Google Scholar 

  20. Pratikakis, I., Zagori, K., Kaddas, P., Gatos, B.: ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018). In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 489–493, August 2018.

  21. Roenko, A.A., Lukin, V.V., Djurović, I., Simeunović, M.: Estimation of parameters for generalized Gaussian distribution. In: 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), pp. 376–379, May 2014.

  22. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000).

    CrossRef  Google Scholar 

  23. Saxena, L.P.: Niblack’s binarization method and its modifications to real-time applications: a review. Artif. Intell. Rev. 1–33 (2017).

    CrossRef  Google Scholar 

  24. Tensmeyer, C., Martinez, T.: Document image binarization with fully convolutional neural networks. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, 9–15 November 2017, pp. 99–104. IEEE (2017).

  25. Wang, C.: Research of image segmentation algorithm based on wavelet transform. In: IEEE International Conference on Computer and Communications (ICCC), pp. 156–160, October 2015.

  26. Wang, R., Li, R., Sun, H.: Haze removal based on multiple scattering model with superpixel algorithm. Signal Process. 127, 24–36 (2016).

    CrossRef  Google Scholar 

  27. Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Formal Pattern Anal. Appl. 6(4), 309–326 (2004).

    MathSciNet  CrossRef  Google Scholar 

  28. Yu, S., Zhang, A., Li, H.: A review of estimating the shape parameter of generalized Gaussian distribution. J. Comput. Inf. Syst. 21(8), 9055–9064 (2012)

    Google Scholar 

  29. Zhang, Y., Wu, J., Xie, X., Li, L., Shi, G.: Blind image quality assessment with improved natural scene statistics model. Digit. Signal Process. 57, 56–65 (2016).

    MathSciNet  CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Krzysztof Okarma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Krupiński, R., Lech, P., Tecław, M., Okarma, K. (2019). Binarization of Degraded Document Images with Generalized Gaussian Distribution. In: , et al. Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science(), vol 11540. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22749-4

  • Online ISBN: 978-3-030-22750-0

  • eBook Packages: Computer ScienceComputer Science (R0)