A Complete Pyramidal Geometrical Scheme for Text Based Image Description and Retrieval

  • Guillaume Joutel
  • Véronique Eglin
  • Hubert Emptoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

This paper presents a general architecture for ancient handwriting documents content description and retrieval. It is based on the Curvelets decomposition of images for indexing linear singularities of handwritten shapes. As it belongs to the Wavelets family, its representation is used at several scales of details. The proposed scheme for handwritten shape characterization targets to detect oriented and curved fragments at different scales: it is used in a first step to extract visual textual interest regions and secondly to compose a cross-scale signature for each handwritten analyzed samples. The images description is studied through different kinds of deformations that show the efficiency of the proposition for even degraded and variable handwriting text. The complete implementation scheme is validated with a content based images retrieval (CBIR) application on the medieval database from the IRHT and on the European 18th century correspondences corpus from the CERPHI.

Keywords

Image Retrieval Document Image Dynamic Time Warping Content Base Image Retrieval Content Base Image Retrieval System 
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.

References

  1. 1.
    Aiolli, F., Simi, M., Sona, D., Sperduti, A., Starita, A., Zaccagnini, G.: SPI: a System for Palaeographic Inspections. AIIA Notizie 4, 34–38 (1999)Google Scholar
  2. 2.
    Antoine, P., Jacques, L.: Measuring a curvature radius with directional wavelets. Inst. Phys. Conf Series, pp. 899–904 (2003)Google Scholar
  3. 3.
    Bensefia, Heutte, L., Paquet, T., Nosary, A.: Identification du scripteur par représentation graphémes. In: CIFED 2002, pp. 285–294 (2002)Google Scholar
  4. 4.
    Boubchir, L., Fadili, J.: Bayesian Denoising Based on the MAP Estimator in Wavelet-domain Using Bessel K Form Prior. In: Proc. of IEEE ICIP 2005; the IEEE International Conference on Image Processing, Genoa, Italy, September 11-14, vol. I, pp. 113–116 (2005)Google Scholar
  5. 5.
    Bulacu, M., Schomaker, L.: Writer Style from Oriented Edge Fragments. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 460–469. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Candés, E., Donoho, D.: Curvelets: A Surprisingly Effective Nonadaptive Representation of Ob-jects with Edges. In: Schumaker, L. (ed.) Curves and surfaces filtering, Vanderbilt University Press (1999)Google Scholar
  7. 7.
    Cha, S.H., Srihari, S.: Multiple Feature Integration for Writer Verification. In: IWFHR VII, pp. 333–342 (2000)Google Scholar
  8. 8.
    Crettez, J.-P.: A set of handwriting families: style recognition. In: ICDAR 1995, pp. 489–494 (1995)Google Scholar
  9. 9.
    Journet, N., Mullot, R., Ramel, J.Y., Eglin, V.: Ancient Printed Documents indexation: a new approach. In: ICAPR 2005. Third International Conference on Advances in Pattern Recognition, Pattern Recognition and Data Mining, Bath, United Kingdom, pp. 513–522 (2005)Google Scholar
  10. 10.
    Loupias, E., Bres, S.: Key Points based Indexing for Pre-attentive Similarities: The KIWI System. Pattern Analysis and Applications, Special Issue Image Indexing (summer 2001)Google Scholar
  11. 11.
    Moalla, I., LeBourgeois, F., Emptoz, H., Alimi, A.M.: Contribution to the Discrimination of the Medieval Manuscript Texts. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 25–37. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    JY. Ramel Google Scholar
  13. 13.
    Shen, C., Ruan, X.G., Mao, T.L.: Writer identification using Gabor, vol. 3, 2061–2064 (2002) [8, 13, 24] Google Scholar
  14. 14.
    Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  15. 15.
    Zhang, B., Srihari, S.N.: Binary vector dissimilarity measures for handwriting identification. In: Document recognition and Retrieval, SPIE, vol. 5010, pp.28–38 (2003)Google Scholar
  16. 16.
    Zhang, B., Srihari, S.N.: Word image retrieval using binary features. In: Document recognition and Retrieval, SPIE, vol. 5296, pp.45–53 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Guillaume Joutel
    • 1
  • Véronique Eglin
    • 1
  • Hubert Emptoz
    • 1
  1. 1.LIRIS UMR CNRS 5205 – INSA Lyon, 69621 VILLEURBANNE Cedex 

Personalised recommendations