Design of an Integrated Environment for the Automated Analysis of Architectural Drawings⋆

  • Philippe Dosch
  • Christian Ah-Soon
  • Gérald Masini
  • Gemma Sénchez1
  • Karl Tombre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1655)


This paper presents the principles which have guided the design of our graphics recognition software environment. A number of applicative modules have been constructed on top of the environment, for the purpose of analyzing architectural drawings. A flexible user interface drives these modules. Our choices are compared with those of similar systems.


Automate Analysis Document Image Grey Level Image Polygonal Approximation Pattern Recognition Letter 
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.
    D. Antoine, S. Collin, and K. Tombre. Analysis of Technical Documents: The REDRAWSystem. In H. S. Baird, H. Bunke, and K. Yamamoto, editors, Structured Document Image Analysis, pages 385–402. Springer-Verlag, Berlin, 1992.Google Scholar
  2. 2.
    C. Ah-Soon and K. Tombre. Network-Based Recognition of Architectural Symbols. In A. Amin, D. Dori, P. Pudil, and H. Freeman, editors, Advances in Pattern Recognition (Proceedings of Joint IAPR Workshops SSPR'98 and SPR'98, Sydney, Australia), Lecture Notes in Computer Science 1451, pages 252–261. Springer-Verlag, Berlin, 1998.Google Scholar
  3. 3.
    A. H. Habacha. Structural Recognition of Disturbed Symbols Using Discrete Relaxation. In Proceedings of 1st International Conference on Document Analysis, Saint-Malo (France), volume 1, pages 170–178, 1991.Google Scholar
  4. 4.
    S. Collin and D. Colnet. Syntactic Analysis of Technical Drawing Dimensions. International Journal of Pattern Recognition and Artificial Intelligence, 8 (5):1131–1148, 1994.CrossRefGoogle Scholar
  5. 5.
    P. Vaxivi`ere and K. Tombre. CELESSTIN: CAD Conversion of Mechanical Drawings. IEEE COMPUTER Magazine, 25 (7):46–54, July 1992.Google Scholar
  6. 6.
    C. Ah-Soon and K. Tombre. Variations on the Analysis of Architectural Drawings. In Proceedings of 4th International Conference on Document Analysis and Recognition, Ulm (Germany), pages 347–351, 1997.Google Scholar
  7. 7.
    K. Tombre, C. Ah-Soon, Ph. Dosch, A. Habed, and G. Masini. Stable, Robust and Off-the-Shelf Methods for Graphics Recognition. In Proceedings of the 14th International Conference on Pattern Recognition, Brisbane (Australia), pages 406–408, 1998.Google Scholar
  8. 8.
    C. Kohl and J. Mundy, The Development of the Image Understanding Environment. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle (USA), 1994.Google Scholar
  9. 9.
    J. Mundy, T. Binford, T. Boult, A. Hanson, R. Beveridge, R. Haralick, V. Ramesh, C. Kohl, D. Lawton, D. Morgan, K. Price, and T. Strat. The Image Understanding Environment Program. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Urbana Champaign (USA), pages 406–416, 1992.Google Scholar
  10. 10.
    B. Meyer, Object-Oriented Software Construction, Second Edition. The Object-Oriented Series. Prentice-Hall, Englewood Cliffs (NJ), USA, 1997.Google Scholar
  11. 11.
    Ø. Due Trier and T. Taxt, Improvement of “Integrated Function Algorithm” for Binarization of Document Images. Pattern Recognition Letters, 16 (3):277–283, 1995.CrossRefGoogle Scholar
  12. 12.
    L. A. Fletcher and R. Kasturi, A Robust Algorithmfor Text String Separation from Mixed Text/Graphics Images. IEEE Transactions on PAMI, 10 (6):910–918, 1988.Google Scholar
  13. 13.
    G. Sanniti di Baja, Well-Shaped, Stable, and Reversible Skeletons fromthe (3,4)-Distance Transform. Journal of Visual Communication and Image Representation, 5 (1):107–115, March 1994.CrossRefGoogle Scholar
  14. 14.
    K. Wall and P. Danielsson. A Fast Sequential Method for Polygonal Approximation of Digitized Curves. Computer Vision, Graphics and Image Processing, 28 (2):220–227, 1984.CrossRefGoogle Scholar
  15. 15.
    P. L. Rosin and G. A. West. Segmentation of Edges into Lines and Arcs. Image and Vision Computing, 7 (2):109–114, May 1989.CrossRefGoogle Scholar
  16. 16.
    D. Dori. Vector-Based Arc Segmentation in the Machine Drawing Understanding SystemEn vironment. In A. L. Spitz and A. Dengel, editors, Document Analysis Systems, pages 338–362. World Scientific, 1995.Google Scholar
  17. 17.
    D. Dori, L. Wenyin, and M. Peleg. How to Win a Dashed Line Detection Contest. In R. Kasturi and K. Tombre, editors, Graphics Recognition—Methods and Applications, Lecture Notes in Computer Science 1072, pages 286–300. Springer-Verlag, Berlin, 1996.Google Scholar
  18. 18.
    G. Sánchez, J. Lladós, and E. Martí. Segmentation and Analysis of Linial Texture in Planes. In Proceedings of 7th Spanish National Symposium on Pattern Recognition and Image Analysis, Barcelona, Spain, volume 1, pages 401–406, 1997.Google Scholar
  19. 19.
    S. W. C. Laman and H. H. S. Ip. Structural Texture Segmentation Using Irregular Pyramid. Pattern Recognition Letters, 15(7):691–698, 1994.Google Scholar
  20. 20.
    M. Maes. Polygonal Shape Recognition Using String-Matching Techniques. Pattern Recognition, 24 (5):433–440, 1991.CrossRefGoogle Scholar
  21. 21.
    W. H. Tsai and S. S. Yu. Attributed String Matching with Merging for Shape Recognition. In Proceedings of 7th International Conference on Pattern Recognition, Montreal (Canada), pages 1162–1164, 1984.Google Scholar
  22. 22.
    Y. T. Tsay and W. H. Tsai. Model-Guided Attributed String Matching by Splitand-Merge for Shape Recognition. International Journal of Pattern Recognition and Artificial Intelligence, 3 (2):159–179, 1989.CrossRefGoogle Scholar
  23. 23.
    D. Dori. Representing Pattern Recognition-Embedded Systems Through Object-Process Diagrams—The Case of the Machine Drawing Understanding System. Pattern Recognition Letters 16 (4):377–384, 1995.CrossRefGoogle Scholar
  24. 24.
    C. Cracknell and A. C. Downton. TABS: Script-Based Software Framework for Research in Image Processing, Analysis and Understanding. IEE Proceedings-Vision, Image and Signal Processing, 145 (3):194–202, 1998.CrossRefGoogle Scholar
  25. 25.
    D. Koelma and A. Smeulders. An Image Processing Library Based on Abstract Image Data-Types in C++. In C. Braccini, L. De Floriani, and G. Vernazza, editors, Proceedings of 8th International Conference on Image Analysis and Processing, San Remo (Italy), Lecture Notes in Computer Science 974, pages 97–102. Springer-Verlag, Berlin, 1995.Google Scholar
  26. 26.
    A. A. S. Sol and A. de Albuquerque Araújo. PhotoPix: An Object-Oriented Framework for Digital Image Processing Systems. In C. Braccini, L. De Floriani, and G. Vernazza, editors, Proceedings of 8th International Conference on Image Analysis and Processing, San Remo (Italy), Lecture Notes in Computer Science 974, pages 109–114. Springer-Verlag, Berlin, 1995.Google Scholar
  27. 27.
    J. Piper and D. Rutovitz. Data Structures for Image Processing in a C Language and UNIX Environment. Pattern Recognition Letters, 3 (2):119–130, 1985.CrossRefGoogle Scholar
  28. 28.
    J. Piper and D. Rutovitz. An Investigation of Object-Oriented Programming as the Basis for an Image Processing and Analysis System. In Proceedings of 9th International Conference on Pattern Recognition, Rome (Italy), pages 1015–1019, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Philippe Dosch
    • 1
  • Christian Ah-Soon
    • 1
  • Gérald Masini
    • 1
  • Gemma Sénchez1
    • 1
    • 2
  • Karl Tombre
    • 1
  1. 1.Loria-Cnrs-Inpl-Inria-UhpVandoeuvre-lès-Nancy CedexFrance
  2. 2.Edifici CCampus Universitat Autònoma de BarcelonaCatalunyaSpain

Personalised recommendations