Resolving Ambiguities in Toponym Recognition in Cartographic Maps

  • Alexander Gelbukh
  • Serguei Levachkine
  • Sang-Yong Han
Conference paper

DOI: 10.1007/978-3-540-25977-0_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3088)
Cite this paper as:
Gelbukh A., Levachkine S., Han SY. (2004) Resolving Ambiguities in Toponym Recognition in Cartographic Maps. In: Lladós J., Kwon YB. (eds) Graphics Recognition. Recent Advances and Perspectives. GREC 2003. Lecture Notes in Computer Science, vol 3088. Springer, Berlin, Heidelberg

Abstract

To date many methods and programs for automatic text recognition exist. However there are no effective text recognition systems for graphic documents. Graphic documents usually contain a great variety of textual information. As a rule the text appears in arbitrary spatial positions, in different fonts, sizes and colors. The text can touch and overlap graphic symbols. The text meaning is semantically much more ambiguous in comparison with standard text. To recognize a text of graphic documents, it is necessary first to separate it from linear objects, solids, and symbols and to define its orientation. Even so, the recognition programs nearly always produce errors. In the context of raster-to-vector conversion of graphic documents, the problem of text recognition is of special interest, because textual information can be used for verification of vectorization results (post-processing). In this work, we propose a method that combines OCR-based text recognition in raster-scanned maps with heuristics specially adapted for cartographic data to resolve the recognition ambiguities using, among other information sources, the spatial object relation-ships. Our goal is to form in the vector thematic layers geographically meaningful words correctly attached to the cartographic objects.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alexander Gelbukh
    • 1
    • 3
  • Serguei Levachkine
    • 2
  • Sang-Yong Han
    • 3
  1. 1.Natural Language Processing Lab 
  2. 2.Image Processing and Pattern Recognition Lab, Centre for Computing Research (CIC) – National Polytechnic Institute (IPN) 
  3. 3.Computer Science and Engineering DepartmentChung-Ang UniversityKorea

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