Advertisement

Local Descriptors for Document Layout Analysis

  • Angelika Garz
  • Markus Diem
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6455)

Abstract

This paper presents a technique for layout analysis of historical document images based on local descriptors. The considered layout elements are regions of regular text and elements having a decorative meaning such as headlines and initials. The proposed technique exploits the differences in the local properties of the layout elements. For this purpose, an approach drawing its inspiration from state-of-the-art object recognition methodologies – namely Scale Invariant Feature Transform (Sift) descriptors – is proposed. The scale of the interest points is used for localization. The results show that the method is able to locate regular text in ancient manuscripts. The detection rate of decorative elements is not as high as for regular text but already yields to promising results.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Miklas, H., Gau, M., Kleber, F., Diem, M., Lettner, M., Vill, M., Sablatnig, R., Schreiner, M., Melcher, M., Hammerschmid, E.G.: St. Catherine’s Monastery on Mount Sinai and the Balkan-Slavic Manuscript Tradition. In: Slovo: Towards a Digital Library of South Slavic Manuscripts, Boyan Penev, pp. 13–36 (2008)Google Scholar
  2. 2.
    Diem, M., Sablatnig, R.: Recognizing Characters of Ancient Manuscripts. In: Proceedings of IS&T SPIE Conference on Computer Image Analysis in the Study of Art (2010) (accepted)Google Scholar
  3. 3.
    Kleber, F., Sablatnig, R., Gau, M., Miklas, H.: Ancient document analysis based on text line extraction. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4 (2008)Google Scholar
  4. 4.
    Likforman-Sulem, L., Zahour, A., Taconet, B.: Text line segmentation of historical documents: a survey. IJDAR 9, 123–138 (2007)CrossRefGoogle Scholar
  5. 5.
    Bourgeois, F.L., Kaileh, H.: Automatic metadata retrieval from ancient manuscripts. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 75–89. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Journet, N., Eglin, V., Ramel, J.Y., Mullot, R.: Text/graphic labelling of ancient printed documents. In: Proc. ICDAR, pp. 1010–1014 (2005)Google Scholar
  7. 7.
    Ramel, J.Y., Leriche, S., Demonet, M.L., Busson, S.: User-driven page layout analysis of historical printed books. IJDAR 9, 243–261 (2007)CrossRefGoogle Scholar
  8. 8.
    Pareti, R., Uttama, S., Salmon, J.P., Ogier, J.M., Tabbone, S., Wendling, L., Adam, S., Vincent, N.: On defining signatures for the retrieval and the classification of graphical drop caps. In: Proc. DIAL (2006)Google Scholar
  9. 9.
    Lindeberg, T.: Scale-Space Theory: A Basic Tool for Analysing Structures at Different Scales. Journal of Applied Statistics 21(2), 224–270 (1994)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Angelika Garz
    • 1
  • Markus Diem
    • 1
  • Robert Sablatnig
    • 1
  1. 1.Institute of Computer Aided Automation, Computer Vision LabVienna University of TechnologyAustria

Personalised recommendations