A Method for Symbol Spotting in Graphical Documents

  • Daniel Zuwala
  • Salvatore Tabbone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)


In this paper we propose a new approach to find symbols in graphical documents. The method is based on a representation of the document in chain points extracted from the skeleton. We merge successively these chain points into a dendrogram framework and according to a measure of density. From the dendrogram, we extract potential symbols which can be recognized after.


Symbol Model Graphical Document Moment Invariant Local View Geometric Moment 
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.


  1. 1.
    Cordella, L.P., Vento, M.: Symbol recognition in documents: a collection of techniques? International Journal on Document Analysis and Recognition 3, 73–88 (2000)CrossRefGoogle Scholar
  2. 2.
    Belkasim, S.O., Shridar, M., Ahmadi, M.: Pattern Recognition with Moment Invariants: A Comparative Study and New Results. Pattern Recognition 24, 1117–1138 (1991)CrossRefGoogle Scholar
  3. 3.
    Belongie, S., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on PAMI 24, 509–522 (2002)Google Scholar
  4. 4.
    Ghorbel, F.: A complete invariant description for gray level images by harmonic analysis approach. Pattern Recognition Letters 15, 1043–1051 (1994)CrossRefGoogle Scholar
  5. 5.
    Lin, B.C., Shen, J.: Fast Computation of Moment Invariants. Pattern Recognition 24, 807–813 (1991)CrossRefGoogle Scholar
  6. 6.
    Llados, J., Jose, J.: Symbol Recognition by Subgraph Matching Between Region Adjancy Graphs. IEEE Transactions on PAMI 23, 1137–1143 (2001)Google Scholar
  7. 7.
    Messmer, B.: Automatic learning and recognition of graphical symbols in engineering drawings. In: Kasturi, R., Tombre, K. (eds.) Graphics Recognition 1995. LNCS, vol. 1072, pp. 123–134. Springer, Heidelberg (1996)Google Scholar
  8. 8.
    Park, B.G., Lee, K.M., Lee, J.: Recognition of partially occluded objects using probabilistic ARG (attributed relational graph)-based matching. Computer Vision and Image Understanding 90, 217–241 (2003)zbMATHCrossRefGoogle Scholar
  9. 9.
    Ramel, J.Y., Emptoz, H.: A structural representation adapted to handwritten symbol recognition. In: Chhabra, A.K., Dori, D. (eds.) GREC 1999. LNCS, vol. 1941, pp. 259–266. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Yan, L., Wenyin, L.: Engineering drawings recognition using a case-based approach. In: International Conference on Document Analysis and Recognition, Edinburgh, vol. 2886, pp. 190–194 (2003)Google Scholar
  11. 11.
    Tabbone, S., Wendling, L., Zuwala, D.: A Hybrid Approach to Detect Graphical Symbols in Documents. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 342–353. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Sanniti di Baja, G.: Well-shaped, stable, and reversible skeletons from the (3,4)-distance transform. Journal of Visual Communication and Image Representation 5(1), 107–115 (1994)CrossRefGoogle Scholar
  13. 13.
    Hu, M.K.: Visual pattern recognition by moment invariants. IEEE Trans. Inform. Theory 8 (1962)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Zuwala
    • 1
  • Salvatore Tabbone
    • 1
  1. 1.Loria-UMR 7503Villers-les-NancyFrance

Personalised recommendations