Verification-based approach for automated text and feature extraction from raster-scanned maps

  • Gregory K. Myers
  • Prasanna G. Mulgaonkar
  • Chien-Huei Chen
  • Jeff L. DeCurtins
  • Edward Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1072)


Existing systems for converting maps to an object-oriented form suitable for a geographic information system (GIS) are only partially automated. Most published approaches for automated interpretation of raster-scanned maps assume that the map is composed of various graphic entities, and that the vast majority of pixel positions on the map each belong to only one type of graphic entity and can therefore be geometrically segmented. However, complex color topographic maps contain several layers of information that overlap substantially (often within a single color plane), making it impossible to geometrically segment the map data into distinct regions containing a single class of graphic object. Here we describe a verification-based approach that uses various knowledge bases to detect, extract, and attribute map features without requiring the presegmentation of graphical entities. This approach builds on SRI International's (SRI's) verification-based computer vision and character recognition methodologies. The approach can also be applied to other types of documents containing a mix of text and graphics, such as engineering drawings, electrical schematics, and technical illustrations.


Automated Map Interpretation Text Extraction Map Feature Extraction 


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  1. 1.
    Boatto, L., V. Consorti, M. Del Buono, S. Di Zenzo, V. Eramo, A. Esposito, F. Melcarne, M. Meucci, A. Morelli, M. Mosciatti, S. Scarci, and M. Tucci. 1992. “An Interpretation System for Land Register Maps,” IEEE Computer, pp. 25–33 (July).Google Scholar
  2. 2.
    Consorti, V., L.P. Cordella, and M. Iaccarino. 1993. “Automatic Lettering of Cadastral Maps,” Proceedings of the Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, pp. 129–132 (20–22 October).Google Scholar
  3. 3.
    Maderlechner, G., H. Mayer, and C. Heipke. 1993, “Conversion of Scanned Cartographic Maps to Geographic Information Systems using Semantic Models,” Proceedings of the Second Annual Symposium on Document Analysis and Information Retrieval, pp. 339–347.Google Scholar
  4. 4.
    Suzuki, S. and T. Yamada. 1990. “MARIS: Map Recognition Input System,” Pattern Recognition, Vol. 23, No. 8, p. 919.Google Scholar
  5. 5.
    den Hartog, J.E., T.K. ten Kate, and J.J. Gebrands. 1995. “Knowledge-Based Segmentation for Automatic Map Interpretation,” Proceedings of the International Workshop on Graphics Recognition, University Park, Pennsylvania, pp. 71–80, (10–11 August).Google Scholar
  6. 6.
    Bolles, R.C., and R.A. Cain. 1982. “Recognizing and Locating Partially Visible Objects,” International Journal of Robotics Research 1, pp. 57–82 (Fall).Google Scholar
  7. 7.
    Chen, C.H., and P.G. Mulgaonkar. 1992. “Automatic Vision Programming,” CVGIP: Image Understanding, Vol. 55, No. 2 (March). 8. Gleason, G.J., and G.J. Agin. 1979. “A Modular Vision System for Sensor-Controlled Manipulation and Inspection,” Proceedings of the Ninth International Symposium on Industrial Robots, Washington, D.C. (March).Google Scholar
  8. 9.
    Shimotsuji, S., O. Hori, and M. Asano. 1994. “Robust Drawing Recognition Based on Model-Guided Segmentation,” Proceedings of Document Analysis Systems, pp. 353–376.Google Scholar
  9. 10.
    Shimotsuji, S., S. Tamura, and S. Tsunekawa. 1989. “Model-based Diagram Analysis by Generally Defined Primitives,” Proceedings of Scandinavian Conference on Image Analysis, pp. 1034–1041.Google Scholar
  10. 11.
    Fischler, M.A., and H.C. Wolf. 1983. “Linear Delineation,” Proceedings of the IEEE CPR-83, pp. 351–356 (June); also, Readings in Computer Vision (M.A. Fischler and O. Firschein, eds.), Morgan Kaufmann, pp. 204–209.Google Scholar
  11. 12.
    Fischler, M.A. 1994. “The Perception of Linear Structure: A Generic Linker,” Proceedings of the ARPA Image Understanding Workshop, pp. 1565–1579.Google Scholar
  12. 13.
    Hontani, H., and S. Shimotsuji. 1995. “Character Detection Based on Multi-Scale Measurement,” Proceedings of ICDAR'95, pp. 644–647.Google Scholar
  13. 14.
    Pierrot-Deseilligny, M., H. LeMen, and G. Stamon. 1995. “Characters String Recognition on Maps, a Method for High Level Reconstruction,” Proceedings of the Third International Conference on Document Analysis and Recognition, Montreal, Canada, Vol. 1, pp. 249–252 (14–16 August).Google Scholar
  14. 15.
    DeCurtins, J.L. 1995. “Keyword Spotting via Word Shape Recognition,” Proceedings of the SPIE Symposium on Electronic Imaging, San Jose, California, Vol. 2422 (February).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Gregory K. Myers
    • 1
  • Prasanna G. Mulgaonkar
    • 1
  • Chien-Huei Chen
    • 1
  • Jeff L. DeCurtins
    • 1
  • Edward Chen
    • 1
  1. 1.Information, Telecommunications, and Automation DivisionSRI InternationalMenlo Park

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