Region Based Approach for Binarization of Degraded Document Images

  • Hubert Michalak
  • Krzysztof OkarmaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 889)


Binarization of highly degraded document images is one of the key steps of image preprocessing, influencing the final results of further text recognition and document analysis. As the contaminations visible on such documents are usually local, the most popular fast global thresholding methods should not be directly applied for such images. On the other hand, the application of some typical adaptive methods based on the analysis of the neighbourhood of each pixel of the images is time consuming and not always leads to satisfactory results. To bridge the gap between those two approaches the application of region based modifications of some histogram based thresholding methods has been proposed in the paper. It has been verified for well known Otsu, Rosin and Kapur algorithms using the challenging images from Bickley Diary dataset. Experimental results obtained for region based Otsu and Kapur methods are superior in comparison to the use of global methods and may be the basis for further research towards combined region based binarization of degraded document images.


Document images Adaptive thresholding Image binarization 


  1. 1.
    Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007)CrossRefGoogle Scholar
  2. 2.
    Chou, C.H., Lin, W.H., Chang, F.: A binarization method with learning-built rules for document images produced by cameras. Pattern Recognit. 43(4), 1518–1530 (2010)CrossRefGoogle Scholar
  3. 3.
    Deng, F., Wu, Z., Lu, Z., Brown, M.S.: Binarizationshop: a user assisted software suite for converting old documents to black-and-white. In: Proceedings of the Annual Joint Conference on Digital Libraries, pp. 255–258 (2010)Google Scholar
  4. 4.
    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, June 2004Google Scholar
  5. 5.
    Gatos, B., Pratikakis, I., Perantonis, S.: Adaptive degraded document image binarization. Pattern Recognit. 39(3), 317–327 (2006)CrossRefGoogle Scholar
  6. 6.
    Kapur, J., Sahoo, P., Wong, A.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)CrossRefGoogle Scholar
  7. 7.
    Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of Niblack inspired binarization methods for ancient documents. In: Document Recognition and Retrieval XVI, vol. 7247, pp. 7247–7247-9 (2009)Google Scholar
  8. 8.
    Kulyukin, V., Kutiyanawala, A., Zaman, T.: Eyes-free barcode detection on smartphones with Niblack’s binarization and Support Vector Machines. In: Proceedings of the 16th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2012) at the World Congress in Computer Science, Computer Engineering, and Applied Computing WORLDCOMP, vol. 1, pp. 284–290. CSREA Press, July 2012Google Scholar
  9. 9.
    Lech, P., Okarma, K.: Fast histogram based image binarization using the Monte Carlo threshold estimation. In: Chmielewski, L.J., Kozera, R., Shin, B.S., Wojciechowski, K. (eds.) Computer Vision and Graphics. Lecture Notes in Computer Science, vol. 8671, pp. 382–390. Springer, Cham (2014)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Lech, P., Okarma, K.: Prediction of the optical character recognition accuracy based on the combined assessment of image binarization results. Elektronika Ir Elektrotechnika 21(6), 62–65 (2015)CrossRefGoogle Scholar
  12. 12.
    Leedham, G., Yan, C., Takru, K., Tan, J.H.N., Mian, L.: Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, ICDAR 2003, pp. 859–864, August 2003Google Scholar
  13. 13.
    Michalak, H., Okarma, K.: Fast adaptive image binarization using the region based approach. In: Silhavy, R. (ed.) Artificial Intelligence and Algorithms in Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 764, pp. 79–90. Springer, Cham (2019)CrossRefGoogle Scholar
  14. 14.
    Moghaddam, R.F., Cheriet, M.: AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recognit. 45(6), 2419–2431 (2012)CrossRefGoogle Scholar
  15. 15.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)Google Scholar
  16. 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)CrossRefGoogle Scholar
  17. 17.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  18. 18.
    Pratikakis, I., Zagoris, K., Barlas, G., Gatos, B.: ICDAR 2017 Document Image Binarization COmpetition (DIBCO 2017) (2017).
  19. 19.
    Rosin, P.L.: Unimodal thresholding. Pattern Recognit. 34(11), 2083–2096 (2001)CrossRefGoogle Scholar
  20. 20.
    Samorodova, O.A., Samorodov, A.V.: Fast implementation of the Niblack binarization algorithm for microscope image segmentation. Pattern Recognit. Image Anal. 26(3), 548–551 (2016)CrossRefGoogle Scholar
  21. 21.
    Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225–236 (2000)CrossRefGoogle Scholar
  22. 22.
    Saxena, L.P.: Niblack’s binarization method and its modifications to real-time applications: a review. Artif. Intell. Rev., 1–33 (2017)Google Scholar
  23. 23.
    Shrivastava, A., Srivastava, D.K.: A review on pixel-based binarization of gray images. Advances in Intelligent Systems and Computing, vol. 439, pp. 357–364. Springer, Singapore (2016)Google Scholar
  24. 24.
    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)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wen, J., Li, S., Sun, J.: A new binarization method for non-uniform illuminated document images. Pattern Recognit. 46(6), 1670–1690 (2013)CrossRefGoogle Scholar
  26. 26.
    Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Form. Pattern Anal. Appl. 6(4), 309–326 (2004)MathSciNetGoogle Scholar
  27. 27.
    Yoon, Y., Ban, K.D., Yoon, H., Lee, J., Kim, J.: Best combination of binarization methods for license plate character segmentation. ETRI J. 35(3), 491–500 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Signal Processing and Multimedia Engineering, Faculty of Electrical EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

Personalised recommendations