Skip to main content
SpringerLink
Log in

A local linear level set method for the binarization of degraded historical document images

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

Document image binarization is a difficult task, especially for complex document images. Nonuniform background, stains, and variation in the intensity of the printed characters are some examples of challenging document features. In this work, binarization is accomplished by taking advantage of local probabilistic models and of a flexible active contour scheme. More specifically, local linear models are used to estimate both the expected stroke and the background pixel intensities. This information is then used as the main driving force in the propagation of an active contour. In addition, a curvature-based force is used to control the viscosity of the contour and leads to more natural-looking results. The proposed implementation benefits from the level set framework, which is highly successful in other contexts, such as medical image segmentation and road network extraction from satellite images. The validity of the proposed approach is demonstrated on both recent and historical document images of various types and languages. In addition, this method was submitted to the Document Image Binarization Contest (DIBCO’09), at which it placed 3rd.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. da Silva J.M.M., Lins R.D., Martins F.M.J., Wachenchauzer R.: A new and efficient algorithm to binarize document images removing back-to-front interference. J. Univers. Comput. Sci. 14(2), 299–313 (2008)

    Google Scholar 

  3. Robert Milewski V.G.: Binarization and cleanup of handwritten text from carbon copy medical form images. Pattern Recognit. 41, 1308–1315 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Trier O., Jain A.: Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1191–1201 (1995). doi:10.1109/34.476511

    Article  Google Scholar 

  6. Farrahi Moghaddam R., Cheriet M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recognit. 43, 2186–2198 (2010). doi:10.1016/j.patcog.2009.12.024

    Article  MATH  Google Scholar 

  7. Drira F., Le Bourgeois F., Emptoz H.: Restoring ink bleed-through degraded document images using a recursive unsupervised classification technique. Doc. Anal. Syst. VII, 38–49 (2006). doi:10.1007/11669487_4

    Google Scholar 

  8. Tan, C.L., Cao, R., Shen, P., Wang, Q., Chee, J., Chang, J.: Removal of interfering strokes in double-sided document images. In: Fifth IEEE Workshop on Applications of Computer Vision, 2000, pp. 16–21. Palm Springs, CA (2000). doi:10.1109/WACV.2000.895397

  9. Su, B., Lu, S., Tan, C.L.: Binarization of historical document images using the local maximum and minimum. In: DAS’10, pp. 159–166. ACM, Boston, Massachusetts (2010) doi:10.1145/1815330.1815351

  10. Cheriet M.: Extraction of handwritten data from noisy gray-level images using a multiscale approach. Int. J. Pattern Recognit. Artif. Intell. 13(5), 665–684 (1999)

    Article  Google Scholar 

  11. Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: Document Recognition and Retrieval XV. San Jose, USA (2008)

  12. Hedjam, R., Farrahi Moghadam, R., Cheriet, M.: Text extraction from degraded document images. In: EUVIP’10, pp. 248–253. Paris, France (2010)

  13. Gatos, B., Ntirogiannis, K., Pratikakis, I.: Icdar 2009 document image binarization contest (dibco 2009). In: ICDAR (2009)

  14. Serra J.: From Pixels to Features, Ch Toggle Mappings, pp. 61–72. Elsevier, Amsterdam (1989)

    Google Scholar 

  15. Naegel B., Wendling L.: A document binarization method based on connected operators. Pattern Recognit. Lett. 31(11), 1251–1259 (2010)

    Article  Google Scholar 

  16. Cao H., Govindaraju V.: Preprocessing of low-quality handwritten documents using markov random fields. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1184–1194 (2009). doi:10.1109/TPAMI.2008.126

    Article  Google Scholar 

  17. Dimov, D., Dimov, A.: Data driven approach to binarization of astronomical images. In: CompSysTech’10, pp. 478–484. ACM, Sofia, Bulgaria (2010). doi:10.1145/1839379.1839465

  18. Bar-Yosef I., Mokeichev A., Kedem K., Dinstein I., Ehrlich U.: Adaptive shape prior for recognition and variational segmentation of degraded historical characters. Pattern Recognit. 42(12), 3348–3354 (2009). doi:10.1016/j.patcog.2008.10.005

    Article  MATH  Google Scholar 

  19. Tan C.L., Cao R., Shen P.: Restoration of archival documents using a wavelet technique. IEEE Trans. Pattern Anal. Mach. Intell. 24(10), 1399–1404 (2002). doi:10.1109/TPAMI.2002.1039211

    Article  Google Scholar 

  20. Rivest-Hénault, D., Cheriet, M.: Unsupervised mri segmentation of brain tissues using a local linear model and level set, Elsevier MRI (online) (2010). doi:10.1016/j.mri.2010.08.007

  21. Gatos, B., Pratikakis, I., Perantonis, S.: Improved document image binarization by using a combination of multiple binarization techniques and adapted edge information. In: ICPR’08, pp. 1–4. (2008)

  22. Farrahi Moghaddam, R., Rivest-Hénault, D., Cheriet, M.: Restoration and segmentation of highly degraded characters using a shape-independent level set approach and multi-level classifiers. In: ICDAR’09, pp. 828–832. Barcelona, Spain (2009). doi:10.1109/ICDAR.2009.107

  23. Farrahi Moghaddam R., Cheriet M.: RSLDI: restoration of single-sided low-quality document images. Pattern Recognit. 42, 3355–3364 (2009)

    Article  MATH  Google Scholar 

  24. Sethian J.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  25. Osher S., Sethian J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79(2), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  26. Malladi R., Sethian J.: A unified approach to noise removal, image enhancement, and shape recovery. IEEE IP 5, 1554–1568 (1996)

    Google Scholar 

  27. Caselles V., Kimmel R., Sapiro G.: Geodesic active contours. Int. J. Comput. Vis. 22, 61–79 (1997)

    Article  MATH  Google Scholar 

  28. Tsai A., Yezzi A., Willsky A.S.: Curve evolution implementation of the Mumford–Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10(8), 1169–1186 (2001)

    Article  MATH  Google Scholar 

  29. Vese L.A., Chan T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. Vol. 50(2), 271–293 (2002)

    Article  MATH  Google Scholar 

  30. Chan T.F., Vese L.: Active contours without edge. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  31. Paragios N., Deriche R.: Geodesic active regions: a new framework to deal with frame partition problems in computer vision. J. Visual Commun. Image Rep. 13(2), 249–268 (2002)

    Article  Google Scholar 

  32. Liu, J.: Robust image segmentation using local median. In: Proceedings of the of the 3rd Canadian Conference on Computer Robot Vision, p. 31. (2006)

  33. Rivest-Hénault D., Cheriet M.: Image segmentation using level set and local linear approximations. In: Kamel, M., Campilho, A. (eds) ICIAR 2007, LNCS 4633, pp. 234–245. Springer, Berlin (2007)

    Google Scholar 

  34. Rosenhahn B., Brox T., Weickert J.: Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int. J. Comput. Vis. 73(3), 243–262 (2007)

    Article  Google Scholar 

  35. Li C., Kao C.-Y., Gore J.C., Ding Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  36. Ben Ayed I., Mitiche A., Belhadj Z.: Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1493–1500 (2006)

    Article  Google Scholar 

  37. Brox T., Cremers D.: On local region models and a statistical interpretation of the piecewise smooth mumford-shah functional. Int. J. Comput. Vis. 84, 184–193 (2009)

    Article  Google Scholar 

  38. Breu H., Gil J., Kirkpatrick D., Werman M.: Linear time euclidean distance transform algorithms. IEEE PAMI 17, 529–533 (1995)

    Article  Google Scholar 

  39. Liu, J., Chelberg, D., Smith, C., Chebrolu, H.: Distribution-based level set segmentation for brain mr images. In: BMVC07. (2007)

  40. Farrahi Moghaddam R., Cheriet M.: Beyond pixels and regions: a non-local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images. Pattern Recognit. 44(2), 363–374 (2011). doi:10.1016/j.patcog.2010.07.027

    Article  Google Scholar 

  41. Haykin S.S.: Blind Deconvolution. PTR Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  42. Farrahi Moghaddam R., Cheriet M.: Low quality document image modeling and enhancement. IJDAR 11(4), 183–201 (2009). doi:10.1007/s10032-008-0076-2

    Article  Google Scholar 

  43. Google, Book Search Dataset, version v Edition (2007)

  44. Deriche, R., Faugeras, O.: Les EDP en traitement des images et vision par ordinateur. Technical Report 2697, INRIA, 1–63 (1996)

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

    Article  Google Scholar 

  46. Cremers, D., Fluck, O., Rousson, M., Aharon, S.: A probabilistic level set formulation for interactive organ segmentation. In: SPIE Medical Imaging. (2007)

  47. Mei, X., Decaudin, P., Hu, B., Zhang, X.: Real-time marker level set on gpu. In: International Conference on Cyberworlds, CW ’08. (2008)

  48. Mackay K., Henderson W., Mackay R.: Introduction to Modern Inorganic Chemistry. CRC Press, Boca Raton (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, 1100 Notre-Dame West, Montréal, QC, H3C 1K3, Canada

    David Rivest-Hénault, Reza Farrahi Moghaddam & Mohamed Cheriet

Authors
  1. David Rivest-Hénault
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Farrahi Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Cheriet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Farrahi Moghaddam.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rivest-Hénault, D., Farrahi Moghaddam, R. & Cheriet, M. A local linear level set method for the binarization of degraded historical document images. IJDAR 15, 101–124 (2012). https://doi.org/10.1007/s10032-011-0157-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-011-0157-5

Keywords

Search

Navigation