Global Discrimination of Graphic Styles

  • Rudolf Pareti
  • Nicole Vincent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3926)


Discrimination between graphical drawings is a difficult problem. It can be considered at different levels according to the applications, details can be observed or more globally what could be called the style. Here we are concerned with a global view of initial letters extracted from early renaissance printed documents. We are going to present a new method to index and classify ornamental letters in ancient books. We show how the Zipf law, originally used in mono-dimensional domains can be adapted to the image domain. We use it as a model to characterize the distribution of patterns occurring in these special drawings that are initial letters. Based on this model some new features are extracted and we show their efficiency for style discrimination.


Grey Level Laser Induce Breakdown Spectroscopy Initial Letter Graphical Drawing False Recognition Rate 
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.


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  1. 1.
    Journet, N., Eglin, N., Ramel, J.Y., Mullot, R.: Text/Graphic labelling of Ancient Printed Documents. In: Proc. Eighth International Conference on Document Analysis and Recognition, pp. 1010–1014 (2005)Google Scholar
  2. 2.
    Eakins, J.P.: Content base image retrieval – can we make it deliver? In: Proc. 2nd UK Conference on image retrieval, Newcastle upon tyne (1999)Google Scholar
  3. 3.
    Melessanaki, K., Papadakis, V., Balas, C., Anglos, D.: Laser induced breakdown spectroscopy and hyper-spectral imaging analysis of pigments on an illuminated manuscript. Spectrochimica Acta Part B 56, 2337–2346 (2001)CrossRefGoogle Scholar
  4. 4.
    Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading (1949)Google Scholar
  5. 5.
    Cameron, A., Cubelli, R., Della Sala, S.: Letter assembling and handwriting share a common allographic code. Journal of Neurolinguistics 15(2), 91–97 (2002)CrossRefGoogle Scholar
  6. 6.
    Dellandrea, E., Makris, P., Vincent, N.: Zipf analysis of audio signals. Fractals 12(1), 73–85 (2004)CrossRefGoogle Scholar
  7. 7.
    Caron, Y., Charpentier, H., Makris, P., Vincent, N.: Power Law Dependencies to Detect Regions of Interest. In: Nyström, I., Sanniti di Baja, G., Svensson, S. (eds.) DGCI 2003. LNCS, vol. 2886, pp. 495–503. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Hartigan, J.A., Wang, M.A.: K-mean clustering Algo. JSTOR revue, 100–108Google Scholar
  9. 9.
    Avilés-Cruz, C., Rangel-Kuoppa, R., Reyes-Ayala, M., Andrade-Gonzalez, A., Escarela-Perez, R.: High-order statistical texture analysis font recognition applied. Pattern Recognition Letters 26(2), 135–145 (2005)CrossRefGoogle Scholar
  10. 10.
    Moalla, I., Lebourgeois, F., Emptoz, H., Alimi, A.M.: Contribution to the Discrimination of the Medieval Manuscript Texts: Application in the Palaeography. Document Analysis Systems, 25–37 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rudolf Pareti
    • 1
  • Nicole Vincent
    • 1
  1. 1.Laboratoire Crip5-SipUniversité Paris 5ParisFrance

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