Skip to main content
SpringerLink
Log in

Binarization of images with variable lighting using adaptive windows

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this article, we present two algorithms for robust binarization of images with variable lighting; we call them Image Binarization with Adaptive Averages and Image Binarization with Adaptive Windows. Both algorithms have a decision criterion based on the local properties of the image to be binarized. This criterion calculates the size of a square window in each pixel of the image on which the characteristics allow us to discriminate to which class each pixel of the image belongs. To calculate the window’s size, a function is optimized that limits the window’s size considering the edges of the light and dark regions. The function is optimized using binary search. In both algorithms, integral images were used to have algorithms with execution times of tenths of a second.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bernsen, J.: Dynamic thresholding of gray-level images. In: Proceedings of Eighth International Conference on Pattern Recognition, Paris (1986)

  2. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  4. Calderon, F., Garnica-Carrillo, A., Flores, J.J.: Multi focus image fusion based on linear combination of images using incremental images. Revista Iberoamericana de Automatica e Informatica Industrial 13(4), 450–461 (2016)

    Article  Google Scholar 

  5. Feng, M.L., Tan, Y.P.: Adaptive binarization method for document image analysis. In: 2004 IEEE International Conference on Multimedia and Expo (ICME)(IEEE Cat. No. 04TH8763), vol. 1, pp. 339–342. IEEE (2004)

  6. Garnica-Carrillo, A., Calderon, F., Flores, J.: Multi-focus image fusion for multiple images using adaptable size windows and parallel programming. In: Signal, Image and Video Processing, pp. 1–8 (2020)

  7. Gatos, B., Pratikakis, I., Perantonis, S.J.: Adaptive degraded document image binarization. Pattern Recogn. 39(3), 317–327 (2006)

    Article  Google Scholar 

  8. Howe, N.R.: Document binarization with automatic parameter tuning. Int. J. Doc. Anal. Recognit. (IJDAR) 16(3), 247–258 (2013)

    Article  Google Scholar 

  9. 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, p. 72470U. International Society for Optics and Photonics (2009)

  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)

    Article  Google Scholar 

  11. Michalak, H., Okarma, K.: Improvement of image binarization methods using image preprocessing with local entropy filtering for alphanumerical character recognition purposes. Entropy 21(6), 562 (2019)

  12. Molina, E., Diaz, J., Hidalgo-Silva, H., Chávez, E.: Algoritmos de binarizacion robusta de imágenes con iluminación no uniforme. Revista iberoamericana de automática e informática industrial (RIAI) 15(3), 252–261 (2018)

    Article  Google Scholar 

  13. Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Englewood Cliffs (1986)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Singh, T.R., Roy, S., Singh, O.I., Sinam, T., Singh, K., et al.: A new local adaptive thresholding technique in binarization. arXiv preprint arXiv:1201.5227 (2012)

  17. Tensmeyer, C., Martinez, T.: Historical document image binarization: a review. SN Comput. Sci. 1, 1–26 (2020)

    Article  Google Scholar 

  18. Valizadeh, M., Kabir, E.: Binarization of degraded document image based on feature space partitioning and classification. Int. J. Doc. Anal. Recognit. (IJDAR) 15(1), 57–69 (2012)

    Article  Google Scholar 

  19. Wolf, C., Jolion, J.M., Chassaing, F.: Text localization, enhancement and binarization in multimedia documents. In: Object Recognition Supported by User Interaction for Service Robots, vol. 2, pp. 1037–1040. IEEE (2002)

Download references

Author information

Authors and Affiliations

  1. Division de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia 403, Col. Centro, C.P. 58000, Morelia, Michoacan, Mexico

    Felix Calderon, Adan Garnica-Carrillo & Cristina Reyes-Zuñiga

Authors
  1. Felix Calderon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adan Garnica-Carrillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cristina Reyes-Zuñiga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Felix Calderon.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Calderon, F., Garnica-Carrillo, A. & Reyes-Zuñiga, C. Binarization of images with variable lighting using adaptive windows. SIViP 16, 1905–1912 (2022). https://doi.org/10.1007/s11760-022-02150-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02150-1

Keywords

Search

Navigation