Advertisement

GOAL: Towards Understanding of Graphic Objects from Architectural to Line Drawings

  • Shyamosree Pal
  • Partha Bhowmick
  • Arindam Biswas
  • Bhargab B. Bhattacharya
Conference paper
  • 515 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)

Abstract

Understanding of graphic objects has become a problem of pertinence in today’s context of digital documentation and document digitization, since graphic information in a document image may be present in several forms, such as engineering drawings, architectural plans, musical scores, tables, charts, extended objects, hand-drawn sketches, etc. There exist quite a few approaches for segmentation of graphics from text, and also a separate set of techniques for recognizing a graphics and its characteristic features. This paper introduces a novel geometric algorithm that performs the task of segmenting out all the graphic objects in a document image and subsequently also works as a high-level tool to classify various graphic types. Given a document image, it performs the text-graphics segmentation by analyzing the geometric features of the minimum-area isothetic polygonal covers of all the objects for varying grid spacing, g. As the shape and size of a polygonal cover depends on g, and each isothetic polygon is represented by an ordered sequence of its vertices, the spatial relationship of the polygons corresponding to a higher grid spacing with those corresponding to a lower spacing, is used for graphics segmentation and subsequent classification. Experimental results demonstrate its efficiency, elegance, and versatility.

Keywords

Grid Spacing Document Image Graphic Type Graphic Object Extended Object 
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.
    Antonacopoulos, A., Ritchings, R.T.: Representation and classification of complex-shaped printed regions using white tiles. In: Proc. ICDAR 1995, pp. 1132–1134 (1995)Google Scholar
  2. 2.
    Biswas, A., Bhowmick, P., Bhattacharya, B.B.: Construction of isothetic covers of a digital object: A combinatorial approach. JVCIR (in press, 2010)Google Scholar
  3. 3.
    Chen, J., Leung, M.K., Gao, Y.: Noisy logo recognition using line segment Hausdorff distance. Pattern Recognition 36(4), 943–955 (2003)CrossRefGoogle Scholar
  4. 4.
    Futrelle, R.P., et al.: Extraction, layout analysis and classification of diagrams in PDF documents. In: ICDAR 2003, pp. 1007–1014 (2003)Google Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, California (1993)Google Scholar
  6. 6.
    Haralick, R.M.: Document image understanding: Geometric and logical layout. In: Proc. CVPR, pp. 385–390 (1994)Google Scholar
  7. 7.
    Hu, J., Kashi, R., Lopresti, D., Wilfong, G.: Evaluating the performance of table processing algorithms 4(3), 140–153 (2002)Google Scholar
  8. 8.
    Klette, R., Rosenfeld, A.: Digital Geometry: Geometric Methods for Digital Picture Analysis. Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  9. 9.
    Kopec, G.E., Chou, P.A.: Document image decoding using Markov source models. IEEE TPAMI 16(6), 602–617 (1994)Google Scholar
  10. 10.
    Li, J., Najmi, A., Gray, R.M.: Image classification by a two-dimensional hidden Markov model. IEEE Trans. Signal Process 48(2), 517–533 (2000)CrossRefGoogle Scholar
  11. 11.
    Pham, T.D.: Unconstrained logo detection in document images. Pattern Recognition 36(12), 3023–3025 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Ramel, J.-Y., Vincent, N.: Strategy for line drawing understanding. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 1–12. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Song, J., et al.: An object-oriented progresssive-simplification-based vectorization system for engineering drawings: Model, algorithm, and performance. IEEE TPAMI 24(8), 1048–1060 (2002)Google Scholar
  14. 14.
    Sun, Z., Wang, W., Zhang, L., Liu, J.: Sketch parameterization using curve approximation. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 334–345. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Phillips, I.T., Haralick, R.M.: Document zone content classification and its performance evaluation. Pattern Recognition 39, 57–73 (2006)CrossRefGoogle Scholar
  16. 16.
    Wenyin, L.: On-line graphics recognition: State-of-the-art. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 291–304. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    Xiao, Y., Yan, H.: Text region extraction in a document image based on the Delaunay tessellation. Pattern Recognition 36(3), 799–809 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Zanibbi, R., Blostein, D., Cordy, J.R.: Recognizing mathematical expressions using tree transformation. IEEE TPAMI 24(11), 1455–1467 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shyamosree Pal
    • 1
  • Partha Bhowmick
    • 1
  • Arindam Biswas
    • 2
  • Bhargab B. Bhattacharya
    • 3
  1. 1.Computer Science and Engineering DepartmentIndian Institute of TechnologyKharagpurIndia
  2. 2.Department of Information TechnologyBengal Engineering and Science UniversityShibpurIndia
  3. 3.Advanced Computing and Microelectronics UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations