A System for Historic Document Image Indexing and Retrieval Based on XML Database Conforming to MPEG7 Standard

  • Wafa Maghrebi
  • Anis Borchani
  • Mohamed A. Khabou
  • Adel M. Alimi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5046)


We present a novel image indexing and retrieval system based on object contour description. Extended curvature scale space (CSS) descriptors composed of both local and global features are used to represent and index concave and convex object shapes. These features are size, rotation, and translation invariant. The index is saved into an XML database conforming to the MPEG7 standard. Our system contains a graphical user interface that allows a user to search a database using either sample or user-drawn shapes. The system was tested using two image databases: the Tunisian National Library (TNL) database containing 430 color and gray-scale images of historic documents, mosaics, and artifacts; and the Squid dataset containing 1100 contour images of fish. Recall and precision rates of 94% and 87%, respectively, were achieved on the TNL database and 71% and 86% on the Squid database. Average response time to a query is about 2.55 sec on a 2.66 GHz Pentium-based computer with 256 Mbyte of RAM.


Image indexing image retrieval eccentricity circularity curvature space descriptors MPEG7 standard XML database 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berretti, S., Del Bimbo, A., Pala, P.: Retrieval by Shape Similarity with Perceptual Distance and Effective Indexing. IEEE Transactions on Multimedia 2, 225–239 (2000)CrossRefGoogle Scholar
  2. 2.
    Berretti, S., Del Bimbo, A., Pala, P.: Efficient Matching and Indexing of Graph Models in Content-Based Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1089–1105 (2001)CrossRefGoogle Scholar
  3. 3.
    Faloutsos, C., Barber, R., Flickner, M., Flickner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and Effective Querying by Image Content. J. Intelligent Information Systems 3, 231–262 (1994)CrossRefGoogle Scholar
  4. 4.
    Pentland, A., Picard, R.W., Sclaroff, S.: Photobook: Tools for Content-Based Manipulation of Image Databases. In: SPIE Proc. Storage and Retrieval for Image and Video Databases II, vol. 2185, pp. 34–47 (1994)Google Scholar
  5. 5.
    Schomaker, L., Vuurpijl, L., Deleau, E.: New Use for the Pen: Outline-Based Image Queries. In: Proc. of the 5th International Conference on Document Analysis and Recognition (ICDAR), Piscataway (NJ), pp. 293–296 (1999)Google Scholar
  6. 6.
    Vuupijl, L., Shomaker, L., Broek, E.: Vind(x): Using the User Through Cooperative Annotation. In: Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR.8), pp. 221–225 (2002) Google Scholar
  7. 7.
    Teague, M.R.: Image Analysis Via the General Theory of Moments. Optical Soc. Am. 70, 920–930 (1980)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and Robust Retrieval By Shape Through Curvature Scale Space. In: Proc. First International Workshop on Image Databases and Multimedia Search, pp. 35–42 (1996)Google Scholar
  9. 9.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Robust and Efficient Shape Indexing Through Curvature Scale Space. In: Proc. British Machine Vision Conference, pp. 53–62 (1996)Google Scholar
  10. 10.
    Kopf, S., Haenselmann, T., Effelsberg, W.: Shape-Based Posture Recognition in Videos. In: Proc. Electronic Image, vol. 5682, pp. 114–124 (2005)Google Scholar
  11. 11.
    National Library of Tunisia,
  12. 12.
    Alimi, A.M.: Evolutionary Computation for the Recognition of On-Line Cursive Handwriting. IETE Journal of Research, Special Issue on Evolutionary Computation in Engineering Sciences 48, 385–396 (2002)Google Scholar
  13. 13.
    Boussellaa, W., Zahour, A., Alimi, A.M.: A Methodology for the Separation of Foreground/Background in Arabic Historical Manuscripts using Hybrid Methods. In: Proc. 22nd Annual Symposium on Applied Computing, Document Engineering Track (2007)Google Scholar
  14. 14.
    Zaghden, N., Charfi, M., Alimi, A.M.: Optical Font Recognition Based on Global Texture Analysis. In: Proc. International Conference on Machine Intelligence, pp. 712–717 (2005)Google Scholar
  15. 15.
    Maghrebi, W., Khabou, M.A., Alimi, A.M.: A System for Indexing and Retrieving Historical Arabic Documents Based on Fourier Descriptors. In: Proc. International Conference on Machine Intelligence, pp. 701–704 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wafa Maghrebi
    • 1
  • Anis Borchani
    • 1
  • Mohamed A. Khabou
    • 2
  • Adel M. Alimi
    • 1
  1. 1.REsearch Group on Intelligent Machines (REGIM)University of SfaxSfaxTunisia
  2. 2.Electrical and Computer Engineering DeptUniversity of West FloridaPensacolaUSA

Personalised recommendations