Advertisement

A Client-Server Architecture for Document Image Recognition

  • Atul K. K. Chhabra
  • Juan F. Arias
  • Theo Pavlidis
  • Phoebe X. Pan
  • Pedro V. Sanders
Conference paper
  • 369 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

We propose a client-server architecture for deploying document image recognition applications, especially graphics recognition applications, in large organizations. An example of such an application is presented. We discuss advantages of client-server techniques over the currently available stand-alone tools for document image recognition.

Keywords

Image Recognition Graphical Editing Recognition Software Client Software Telephone Company 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R. Kasturi, S. T. Bow, W. El-Masri, J. R. Gattiker, and U. B. Mokate. A system for interpretation of line drawings. IEEE Trans. Pattern Analysis and Machine Intelligence, 12(10):978–992, 1990. 133CrossRefGoogle Scholar
  2. 2.
    D. Antoine. CIPLAN: A model-based system with original features for understanding French plats. In Proc. 1st International Conference on Document Analysis and Recognition, pages 647–655, St. Malo, Paris, 1991. 133Google Scholar
  3. 3.
    D. Antoine, S. Collin, and K. Tombre. Analysis of technical documents: The REDRAW System. In H. S. Baird, H. Bunke, and K. Yamamoto, editors, Structured Document Image Analysis, pages 385–402. Springer Verlag, Berlin/Heidelberg, 1992. 133Google Scholar
  4. 4.
    S. H. Joseph and T. P. Pridmore. Knowledge-directed interpretation of mechanical engineering drawings. IEEE Trans. Pattern Analysis and Machine Intelligence, 14(9):928–940, 1992. 133CrossRefGoogle Scholar
  5. 5.
    P. Vaxivière and K. Tombre. CELESSTIN: CAD conversion of mechanical drawings. IEEE Computer Magazine, 25(7):46–54, July 1992. 133Google Scholar
  6. 6.
    L. Wenyin and D. Dori. Automated CAD conversion with the machine drawing understanding system. In Proc. IAPR Workshop on Document Analysis Systems, pages 241–259, Malvern, PA, USA, October 1996. 133Google Scholar
  7. 9.
    J. F. Arias, A. Prasad, R. Kasturi, and A. Chhabra. Interpretation of telephone company central office equipment drawings. In Proc. 12th IAPR International Conference on Pattern Recognition, pages B310–B314, Jerusalem, Israel, October 1994. 135Google Scholar
  8. 10.
    J. F. Arias, S. Balasubramanian, A. Prasad, R. Kasturi, and A. Chhabra. Information extraction from telephone company drawings. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 729–732, Seattle, Washington, June 1994. 135Google Scholar
  9. 11.
    J. F. Arias, R. Kasturi, and A. Chhabra. Efficient techniques for telephone company line drawing interpretation. In Proc. of 3nd Int. Conf. on Document Analysis and Recognition, pages 795–798, Montréal, Canada, August 1995. 135Google Scholar
  10. 12.
    J. F. Arias, A. Chhabra, and V. Misra. Interpreting and representing tabular documents. In Proc. of CVPR, pages 600–605, San Francisco, CA, June 1996. 135Google Scholar
  11. 13.
    J. F. Arias, A. Chhabra, and V. Misra. Efficient interpretation of tabular documents. In Proc. International Conference on Pattern Recognition, volume III, pages 681–685, Vienna, Austria, August 1996. 135Google Scholar
  12. 14.
    H. Luo, R. Kasturi, J. F. Arias, and A. Chhabra. Interpretation of lines in distributing frame drawings. In Proc. International Conference on Document Analysis and Recognition, volume I, pages 66–70, Ulm, Germany, August 1997. 135Google Scholar
  13. 15.
    J. F. Arias, A. Chhabra, and V. Misra. A practical application of graphics recognition: Helping with the extraction of information from telephone company drawings. In K. Tombre and A. Chhabra, editors, Graphics Recognition — Algorithms and Systems, volume 1389 of Lecture Notes in Computer Science, pages 314–321. Springer-Verlag, Berlin, Germany, 1998. 135Google Scholar
  14. 16.
    A. K. Chhabra, V. Misra, and J. Arias. Detection of horizontal lines in noisy run length encoded images: The FAST method. In R. Kasturi and K. Tombre, editors, Graphics Recognition — Methods and Applications, volume 1072 of Lecture Notes in Computer Science, pages 35–48. Springer-Verlag, Berlin, Germany, 1996. 135Google Scholar
  15. 17.
    J. F. Arias, A. Chhabra, and V. Misra. Finding straight lines in drawings. In Proc. International Conference on Document Analysis and Recognition, volume II, pages 788–791, Ulm, Germany, August 1997. 135Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Atul K. K. Chhabra
    • 1
  • Juan F. Arias
    • 1
  • Theo Pavlidis
    • 2
  • Phoebe X. Pan
    • 2
  • Pedro V. Sanders
    • 2
  1. 1.Verizon CommunicationsWhite Plains, NYUSA
  2. 2.Department of Computer ScienceState University of New York at Stony BrookStony Brook, NYUSA

Personalised recommendations