GREC’09 Arc Segmentation Contest: Performance Evaluation on Old Documents

  • Hasan S. M. Al-Khaffaf
  • Abdullah Z. Talib
  • Mohd Azam Osman
  • Poh Lee Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)


Empirical performance evaluation of raster to vector methods is an important topic in the area of graphics recognition. By studying automatic vectorization methods we can reveal the maturity of the tested methods whether as a research prototype or a commercial software. Arc Segmentation Contest held in conjunction with the eighth IAPR International Workshop on Graphics Recognition (GREC’09) is an excellent opportunity for researchers to present the results of their proposed raster to vector methods. The contest provides a uniform platform where the output of different methods can be analyzed. The relevance of the contest is further revealed by the creation of new test images with their ground truth data. Old documents were used in this contest. Five methods participated (two research prototypes and three commercial software). Two tests were performed namely between-methods test (participated by all methods) and within-method test (participated by only one method). This paper presents the results of the contest.


Performance Evaluation Graphics Recognition Raster to Vector Conversion Methods Line Drawings 


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  1. 1.
    Kong, B., Phillips, I., Haralick, R., Prasad, A., Kasturi, R.: A benchmark: Performance evaluation of dashed-line detection algorithms. In: Kasturi, R., Tombre, K. (eds.) Graphics Recognition 1995. LNCS, vol. 1072, pp. 270–285. Springer, Heidelberg (1996)Google Scholar
  2. 2.
    Chhabra, A.K., Phillips, I.T.: The second international graphics recognition contest - raster to vector conversion: A report. In: Chhabra, A.K., Tombre, K. (eds.) GREC 1997. LNCS, vol. 1389, pp. 390–410. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Chhabra, A.K., Phillips, I.T.: Performance evaluation of line drawing recognition systems. In: Proc. 15th International Conference on Pattern Recognition, Barcelona, vol. 4, pp. 864–869 (2000)Google Scholar
  4. 4.
    Tombre, K.: International workshop on graphics recognition - dashed-line detection contest, (accessed in June 2009)
  5. 5.
    Phillips, I.T., Chhabra, A.K.: Empirical performance evaluation of graphics recognition systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 849–870 (1999)CrossRefGoogle Scholar
  6. 6.
    Liu, W.Y., Zhai, J., Dori, D.: Extended summary of the arc segmentation contest. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 343–349. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Liu, W.: Report of the arc segmentation contest. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 364–367. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Wenyin, L.: The third report of the arc segmentation contest. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 358–361. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Shafait, F., Keysers, D., Breucl, T.M.: GREC 2007 arc segmentation contest: Evaluation of four participating algorithms. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 310–320. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Shafait, F., Keysers, D., Breuel, T.M.: Pixel-accurate representation and evaluation of page segmentation in document images. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 1, pp. 872–875 (2006)Google Scholar
  11. 11.
    Liu, W.Y., Dori, D.: A protocol for performance evaluation of line detection algorithms. Machine Vision and Applications 9(5-6), 240–250 (1997)CrossRefGoogle Scholar
  12. 12.
    Peatfield, A.E.: Teach Yourself Mechanical Engineering. E.L.B.S. and English Universities Press, London (1965)Google Scholar
  13. 13.
    Kurtz, M.: Mechanical Engineers’ Handbook. John Wiley & Sons, Chichester (1986)Google Scholar
  14. 14.
    ImLab: Free image processing system,
  15. 15.
    Liu, W.Y., Dori, D.: Incremental arc segmentation algorithm and its evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(4), 424–431 (1998)CrossRefGoogle Scholar
  16. 16.
    Lamiroy, B., Guebbas, Y.: Robust Circular Arc Detection. In: Ogier, J.-M., Liu, W., Lladós, J. (eds.) GREC 2009. LNCS, vol. 6020. Springer, Heidelberg (2009)Google Scholar
  17. 17.
    VPstudio: Raster to vector conversion software, softelec, Munich, Germany, and
  18. 18.
    Scan2CAD: Raster to vector conversion software, softcover international limited, Cambridge, England,
  19. 19.
    Vectory: Vectorization software, graphikon gmbh, Berlin, Germany,
  20. 20.
    Hilaire, X., Tombre, K.: Robust and accurate vectorization of line drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(6), 890–904 (2006)CrossRefGoogle Scholar
  21. 21.
    Jiqiang, S., Feng, S., Chiew-Lan, T., Shijie, C.: An object-oriented progressive-simplification-based vectorization system for engineering drawings: model, algorithm, and performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1048–1060 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hasan S. M. Al-Khaffaf
    • 1
  • Abdullah Z. Talib
    • 1
  • Mohd Azam Osman
    • 1
  • Poh Lee Wong
    • 1
  1. 1.School of Computer SciencesUniversiti Sains MalaysiaUSM PenangMalaysia

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