A Comprehensive Survey on Image Binarization Techniques

Part of the Studies in Computational Intelligence book series (SCI, volume 560)


A detailed survey about the principles of image binarization techniques is introduced in this chapter. A comprehensive review is given. A number of classical methodologies together with the recent works are considered for comparison and study of the concept of binarization for both document and graphic images.


Review of binarization methods Global binarization Image thresholding Adaptive local binarization 


  1. 1.
    Moghaddam, R.F., Cheriet, M.: AdOtsu: an adaptive and parameter less generalization of Otsu’s method for document image binarization. Pattern Recogn. 45(6), 2419–2431 (2012)CrossRefGoogle Scholar
  2. 2.
    Gatos, B., Pratikakis, I., Perantonis, S.J.: Adaptive degraded document image binarization. Pattern Recogn. 39(3), 317–327 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Ntirogiannis, K., Gatos, B., Pratikakis, I.: Performance evaluation methodology for historical document image binarization. IEEE Trans. Image Process. 22(2), 595–609 (2013)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Ntirogiannis, K., Gatos, B., Pratikakis, I.: A combined approach for the binarization of handwritten document images. Pattern Recogn. Lett. 35, 3–15 (2014). (ISSN 0167-8655,
  5. 5.
    Valizadeh, M., Kabir, E.: Binarization of degraded document image based on feature space partitioning and classification. Int. J. Doc. Anal. Recogn. (IJDAR) 15(1), 57–69 (2012)CrossRefGoogle Scholar
  6. 6.
    Hedjam, R., Moghaddam, R.F., Cheriet, M.: A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images. Pattern Recogn. 44(9), 2184–2196 (2011)CrossRefGoogle Scholar
  7. 7.
    Bataineh, B., Abdullah, S.N.H.S., Omar, K.: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recogn. Lett. 32(14), 1805–1813 (2011)CrossRefGoogle Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Bernsen, J.: Dynamic thresholding of gray level images. In: Proceedings of International Conference on Pattern Recognition (ICPR), pp. 1251–1255 (1986)Google Scholar
  10. 10.
    Gatos, B., Ntirogiannis, K., Perantonis S.J.: Improved document image binarization by using a combination of multiple binarization techniques and adapted edge information. In: Proceedings of 19th International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  11. 11.
    Johannsen, G., Bille, J.: A threshold selection method using information measures. In: 6th International Conference on Pattern Recognition, pp. 140–143 (1982)Google Scholar
  12. 12.
    Kapur, N.J., Sahoo, P.K., Wong, C.K.A.: A new method for gray-level picture thresholding using the entropy of the histogram. J. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)CrossRefGoogle Scholar
  13. 13.
    Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)CrossRefGoogle Scholar
  14. 14.
    Niblack, W.: An introduction to digital image processing, pp. 115–116. Prentice Hall, Eaglewood Cliffs (1986)Google Scholar
  15. 15.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recogn. 19(1), 41–47 (1986)CrossRefGoogle Scholar
  16. 16.
    Ridler, T., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cyber. 8(8), 630–632 (1978)CrossRefGoogle Scholar
  17. 17.
    Moghaddam, R.F., Cheriet, M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn. 43(6), 2186–2198 (2010)CrossRefzbMATHGoogle Scholar
  18. 18.
    Lopes, N.V., Mogadouro do Couto, P.A., Bustince, H., Melo-Pinto, P.: Automatic histogram threshold using fuzzy measures. IEEE Trans. Image Process. 19(1), 199–204 (2010)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Pai, Y.T., Chang, Y.F., Ruan, S.J.: Adaptive thresholding algorithm: efficient computation technique based on intelligent block detection for degraded document images. Pattern Recogn. 43(9), 3177–3187 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhou, Z., Li, L., Tan, C.L.: Edge based binarization for video text images. In: Proceedings of 20th International Conference on Pattern Recognition (ICPR), pp. 133–136 (2010)Google Scholar
  21. 21.
    Ntirogiannis, K., Gatos, B., Pratikakis, I.: A modified adaptive logical level binarization technique for historical document images. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1171–1175 (2009)Google Scholar
  22. 22.
    Stathis, P., Kavallieratou, E., Papamarkos, N.: An evaluation technique for binarization algorithms. J. Univers. Comput. Sci. 14(18), 3011–3030 (2008)Google Scholar
  23. 23.
    Anjos, A., Shahbazkia, H.: Bi-level image thresholding—a fast method. Biosignals 2, 70–76 (2008)Google Scholar
  24. 24.
    Ntirogiannis, K., Gatos, B., Pratikakis, I.: An objective evaluation methodology for document image binarization techniques. In: 8th IAPR Workshop on Document Analysis Systems (2008)Google Scholar
  25. 25.
    Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007)CrossRefGoogle Scholar
  26. 26.
    Cheriet, M., Moghaddam, R.F., Hedjam, R.: A learning framework for the optimization and automation of document binarization methods. Comput. Vis. Image Underst. (CVIU) 117(3), 269–280 (2013)CrossRefGoogle Scholar
  27. 27.
    Su, B., Lu, S., Tan, C.L.: Robust document image binarization technique for degraded document images. IEEE Trans. Image Process. 22(4), 1408–1417 (2013)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Morteza, V., Ehsanollah, K.: An adaptive water flow model for binarization of degraded document images. Int. J. Doc. Analysis Recogn. (IJDAR) 16(2), 165–176 (2013)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Computer Science and EngineeringUniversity of CalcuttaKolkataIndia
  2. 2.A. K. Choudhury School of Information TechnologyUniversity of CalcuttaKolkataIndia
  3. 3.Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland

Personalised recommendations