Skip to main content

Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use

  • Conference paper
Graphics Recognition. Recent Advances and Perspectives (GREC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3088))

Included in the following conference series:

Abstract

This paper presents a vectorisation system based on the use of strategic knowledge. This one is composed of two parts: a processing library and a graphic user interface. Our processing library is composed of image pre-processing and vectorisation tools. Our graphic user interface is used for the strategic knowledge acquisition and operationalisation. It allows to construct and to execute scenarios, exploiting any processing of our library, according to documents’ contexts and users’ adopted strategies. A XML data representation is used, allowing an easy data manipulation. A scenario example is presented for graphics recognition on utility maps.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ablameyko, S., Pridmore, T.P.: Machine Interpretation of Line Drawing Images. Springer, Heidelberg (2000)

    Google Scholar 

  2. Adam, S., Ogier, J.M., Cariou, C., Gardes, J., Lecourtier, Y.: Combination of Invariant Pattern Recognition Primitive on Technical Documents. In: Graphics Recognition, GREC (1999)

    Google Scholar 

  3. Burge, M., Kropatsh, W.G.: A Minimal Line Property Preserving Representation of Line Images. Structural and Syntactical Pattern Recognition, SSPR (1998)

    Google Scholar 

  4. Delalandre, M., Nicolas, S., Trupin, E., Ogier, J.M.: Symbols Recognition by Global-Local Structural Approaches, Based on the Scenarios Use, and with a XML Representation of Data. In: International Conference on Document Analysis and Recognition, ICDAR (2003)

    Google Scholar 

  5. Delalandre, M., Trupin, É., Ogier, J.-M.: Local Structural Analysis: A Primer. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 223–234. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Di Baja, G.B.: Well shaped, Stable, and Reversible Skeletons from the 3-4 Distance Transform. Journal of Visual Communication and Image Representation 5(1), 107–115 (1992)

    Article  Google Scholar 

  7. Fan, J.: Off-line Optical Character Recognition for Printed Chinese Character-A Survey. Technical Report, University of Colombia, USA (2002)

    Google Scholar 

  8. Den Hartog, J.E.: Knowledge Based Interpretation of Utility Maps. Computer Vision and Image Understanding (CVIU) 63(1), 105–117 (1996)

    Article  Google Scholar 

  9. Henderson, T.C., Swaminathan, L.: Agent Based Engineering Drawing Analysis. In: Symposium on Document Image Understanding Technology, SDIUT (2003)

    Google Scholar 

  10. Kittler, J., Illingworth, J.: Minimum Error Thresholding. Pattern Recognition (PR) 19(1), 41–47 (1986)

    Article  Google Scholar 

  11. Lassaulzais, A., Mullot, R., Gardes, J., Lecourtier, Y.: Segmentation d’Infrastructures de Réseau Téléphonique. Colloque International Francophone sur l’Ecrit et le Document, CIFED (1998)

    Google Scholar 

  12. Liao, C.W., Huang, J.S.: Stroke Segmentation by Bernstein-Bezier Curve Fitting. Pattern Recognition (PR) 23(5), 475–484 (1990)

    Article  Google Scholar 

  13. Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances an Perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, p. 104. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  14. El-Mejbri, E.F., Grabowski, H., Kunze, H., Lossack, R.S., Michelis, A.: A Contribution to the Reconstruction Process of Article Based Assembly Drawings. In: Graphics Recognition, GREC (2001)

    Google Scholar 

  15. Ogier, J.M., Olivier, C., Lecourtier, Y.: Extraction of Roads from Digitized Maps. In: European Signal Processing Conference, EUSIPCO (1992)

    Google Scholar 

  16. Ogier, J.M., Adam, S., Bessaid, A., Bechar, H.: Automatic Topographic Color Map Analysis. System. In: Graphics Recognition, GREC (2001)

    Google Scholar 

  17. Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. Transactions on Systems, Man and Cybernetics (TSMC) 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  18. Parker, J.R.: Algorithms for Image Processing and Computer Vision. Paperback editions (1996)

    Google Scholar 

  19. Pavlidis, T., Horowitz, S.L.: Segmentation of Plane Curves. Transactions on Computers (TC) 23, 860–870 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ramer, V.: An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Vision Graphics and Image Processing 1(3), 244–246 (1972)

    Article  Google Scholar 

  21. Rosin, P.L., West, A.W.: Nonparametric Segmentation of Curves Into Various Representations. Pattern Analysis and Machine Intelligence (PAMI) 17(12), 1140–1153 (1995)

    Article  Google Scholar 

  22. Saidali, Y., Adam, S., Ogier, J.M., Trupin, E., Labiche, J.: Knowledge Representation and Acquisition for Engineering Document Analysis. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 25–37. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  23. Song, J., Su, F., Tai, C., Cai, S.: An Object-Oriented Progressive-Simplification based Vectorisation System for Engineering Drawings: Model, Algorithm and Performance. Pattern Analysis and Machine Intelligence (PAMI) 24(8), 1048–1060 (2002)

    Article  Google Scholar 

  24. Taconet, B., Zahour, A., Zhang, S., Faure, A.: Deux Algorithmes de Squelettisation. Reconnaissance Automatique de l’Ecriture, RAE (1990)

    Google Scholar 

  25. Tombre, K.: Ten Years of Research in the Analysis of Graphics Documents, Achievements and Open Problems. Image Processing and Image Understanding (1998)

    Google Scholar 

  26. Tombre, K., Ah-Soon, C., Dosch, P., Masini, G., Tabbone, S.: Stable and Robust Vectorisation: How to Make the Right Choices. In: Graphics Recognition, GREC (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Delalandre, M., Saidali, Y., Trupin, E., Ogier, JM. (2004). Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use. In: Lladós, J., Kwon, YB. (eds) Graphics Recognition. Recent Advances and Perspectives. GREC 2003. Lecture Notes in Computer Science, vol 3088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25977-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-25977-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22478-5

  • Online ISBN: 978-3-540-25977-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics