Segmenting and Indexing Old Documents Using a Letter Extraction

  • Mickael Coustaty
  • Sloven Dubois
  • Jean-Marc Ogier
  • Michel Menard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)


This paper presents a new method to extract areas of interest in drop caps and particularly the most important shape: Letter itself. This method relies on a combination of a Aujol and Chambolle algorithm and a Segmentation using a Zipf Law and can be enhanced as a three-step process: 1)Decomposition in layers 2)Segmentation using a Zipf Law 3)Selection of the connected components.


Document Image Image Decomposition Total Variation Minimization Important Shape Oscillate Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AAFC05]
    Aujol, J.F., Aubert, G., Blanc Feraud, L., Chambolle, A.: Image decomposition into a bounded variation component and an oscillating component. Journal of Mathematical Imaging and Vision 22(1), 71–88 (2005)CrossRefMathSciNetGoogle Scholar
  2. [AC05]
    Aujol, J.-F., Chambolle, A.: Dual norms and image decomposition models. International Journal of Computer Vision 63(1), 85–104 (2005)CrossRefMathSciNetGoogle Scholar
  3. [AGCO06]
    Aujol, J.-F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition - modeling, algorithms, and parameter selection. International Journal of Computer Vision 67(1), 111–136 (2006)CrossRefGoogle Scholar
  4. [AK06]
    Aujol, J.-F., Kang, S.H.: Color image decomposition and restoration. J. Visual Communication and Image Representation 17(4), 916–928 (2006)CrossRefGoogle Scholar
  5. [BC07]
    Bresson, X., Chan, T.: Fast minimization of the vectorial total variation norm and applications to color image processing. SIAM Journal on Imaging Sciences (SIIMS) (submitted, 2007)Google Scholar
  6. [DAV08]
    Duval, V., Aujol, J.-F., Vese, L.: A projected gradient algorithm for color image decomposition. Technical report, CMLA Preprint 2008-21 (2008)Google Scholar
  7. [DLPM08]
    Dubois, S., Lugiez, M., Péteri, R., Ménard, M.: Adding a noise component to a color decomposition model for improving color texture extraction. In: CGIV 2008 and MCS 2008 Final Program and Proceedings, pp. 394–398 (2008)Google Scholar
  8. [HSO+08]
    Chouaib, H., Tabbone, S., Ramos, O., Cloppet, F., Vincent, N.: Feature selection combining genetic algorithm and adaboost classifiers. In: ICPR 2008, Florida (2008)Google Scholar
  9. [JT08]
    Jouili, S., Tabbone, S.: Applications des graphes en traitement d’images. In: ROGICS 2008, Mahdia Tunisia, pp. 434–442. University of Ottawa, University of Sfax, Canada, Tunisia (2008)Google Scholar
  10. [LSE92]
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal. Physica D 60, 259–269 (1992)zbMATHCrossRefGoogle Scholar
  11. [McQ67]
    McQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: University of California Press (ed.) Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297 (1967)Google Scholar
  12. [Mey01]
    Meyer, Y.: Oscillating patterns in image processing and nonlinear evolution equations. The fifteenth dean jacqueline B. Lewis Memorial Lectures (2001)Google Scholar
  13. [OSV03]
    Osher, S.J., SoIe, A., Vese, L.A.: Image decomposition, image restoration, and texture modeling using total variation minimization and the H-1 norm. In: International Conference on Image Processing, pp. 689–692 (2003)Google Scholar
  14. [PV06]
    Pareti, R., Vincent, N.: Ancient initial letters indexing. In: ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition, Washington, DC, USA, pp. 756–759. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  15. [SED05]
    Starck, J.L., Elad, M., Donoho, D.L.: Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans. Image Processing 14(10), 1570–1582 (2005)CrossRefMathSciNetGoogle Scholar
  16. [ULDO05]
    Uttama, S., Loonis, P., Delalandre, M., Ogier, J.-M.: Segmentation and retrieval of ancient graphic documents. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 88–98. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. [VO04]
    Vese, L.A., Osher, S.J.: Image denoising and decomposition with total variation minimization and oscillatory functions. Journal of Mathematical Imaging and Vision 20(1-2), 7–18 (2004)CrossRefMathSciNetGoogle Scholar
  18. [VO06]
    Vese, L.A., Osher, S.: Color texture modeling and color image decomposition in a variational-PDE approach. In: SYNASC, pp. 103–110. IEEE Computer Society, Los Alamitos (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mickael Coustaty
    • 1
  • Sloven Dubois
    • 1
  • Jean-Marc Ogier
    • 1
  • Michel Menard
    • 1
  1. 1.L3i LaboratoryLa RochelleFrance

Personalised recommendations