A Few Steps Towards On-the-Fly Symbol Recognition with Relevance Feedback

  • Jan Rendek
  • Bart Lamiroy
  • Karl Tombre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)


This paper presents some first steps in building an interactive system which allows a user to efficiently browse a large set of scanned documents, without prior knowledge on the content of these documents, and retrieving symbols of interest to him personally, through a relevance feedback mechanism.


  1. 1.
    Chhabra, A.K.: Graphic Symbol Recognition: An Overview. In: Tombre, K., Chhabra, A.K. (eds.) GREC 1997. LNCS, vol. 1389, pp. 68–79. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances and Perspectives. In: Blostein, D., Kwon, Y.B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–127. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Lin, F., Zhang, B.: Support vector machine learning for image retrieval. In: Proceedings of IEEE International Conference on Image Processing, pp. 721–724 (2001)Google Scholar
  5. 5.
    Zhou, X., Huang, T.S.: Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems 8, 536–544 (2003)CrossRefGoogle Scholar
  6. 6.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on PAMI 22, 1349–1380 (2000)Google Scholar
  7. 7.
    Vasconcelos, N., Lippman, A.: Bayesian Relevance Feedback for Content-Based Image Retrieval. In: Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, p. 63 (2000)Google Scholar
  8. 8.
    Doermann, D.: The Indexing and Retrieval of Document Images: A Survey. Computer Vision and Image Understanding 70, 287–298 (1998)CrossRefGoogle Scholar
  9. 9.
    Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Query databases through multiple examples. In: Very Large Databases (1998)Google Scholar
  10. 10.
    Rui, Y., Huang, T.: Optimizing Learning in Image Retrieval. In: Computer Vision and Pattern Recognition, p. 1236 (2000)Google Scholar
  11. 11.
    Rui, Y., Huang, T., Mehrotra, S.: Content-Based Image Retrieval with Relevance Feedback in MARS. In: Proceedings of IEEE International Conference on Image Processing, pp. 815–818 (1997)Google Scholar
  12. 12.
    Su, Z., Zhang, H., Ma, S.: Using Bayesian Classifier in Relevant Feedback of Image Retrieval. In: 12th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2000 (2000)Google Scholar
  13. 13.
    Zhang, H.J., Chen, Z., Liu, W.Y., Li, M.: Relevance feedback in content-based image search. World Wide Web 2, 131–155 (2003)CrossRefGoogle Scholar
  14. 14.
    Onada, T., Murata, M., Yamada, S.: Relevance feedback document retrieval using support vector machines. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2003), pp. 1757–1762 (2003)Google Scholar
  15. 15.
    MacArthur, S.D., Brodley, C.E., Shyu, C.: Relevance Feedback Decision Trees in Content-Based Image Retrieval. In: IEEE Workshop on Content-based Access of Image and Video Libraries, p. 68 (2000)Google Scholar
  16. 16.
    Wang, T., Rui, Y., Hu, S., Sun, J.: Adaptive Tree Similarity for Image Retrieval. Multimedia Systems 9, 131–143 (2003)CrossRefGoogle Scholar
  17. 17.
    Giacinto, G., Roli, F.: Nearest-prototype relevance feedback for content based image retrieval. In: Proceedings of the 17th International Conference on Pattern Recognition, Cambridge (UK), pp. 989–992 (2004)Google Scholar
  18. 18.
    Giacinto, G., Roli, F.: Bayesian relevance feedback for content-based image retrieval. Pattern Recognition 37, 1499–1508 (2004)zbMATHCrossRefGoogle Scholar
  19. 19.
    Nagy, G., Seth, S.: Hierarchical Representation of Optically Scanned Documents. In: Proceedings of 7th International Conference on Pattern Recognition, Montréal (Canada), pp. 347–349 (1984)Google Scholar
  20. 20.
    Appiani, E., Cesarini, F., Colla, A.M., Diligenti, M., Gori, M., Marinai, S., Soda, G.: Automatic document classification and indexing in high-volume applications. International Journal on Document Analysis and Recognition 4, 69–83 (2001)CrossRefGoogle Scholar
  21. 21.
    Tombre, K., Tabbone, S., Pélissier, L., Lamiroy, B., Dosch, P.: Text/graphics separation revisited. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 200–211. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Manmatha, R., Rothfeder, J.L.: A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents. IEEE Transactions on PAMI 27, 1212–1225 (2005)Google Scholar
  23. 23.
    Liao, S.X., Pawlak, M.: On the Accuracy of Zernike Moments for Image Analysis. IEEE Transactions on PAMI 20, 1358–1364 (1998)Google Scholar
  24. 24.
    Hse, H., Newton, A.R.: Sketched Symbol Recognition using Zernike Moments. In: Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jan Rendek
    • 1
    • 2
  • Bart Lamiroy
    • 1
  • Karl Tombre
    • 1
  1. 1.LORIA–INPLVandœuvre-lès-NancyFrance
  2. 2.France Télécom R&DMeylan CEDEXFrance

Personalised recommendations