Resolving Ambiguities in Toponym Recognition in Cartographic Maps
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.
Unable to display preview. Download preview PDF.
- 3.Nagy, G.: Twenty Years of Document Image Analysis in PAMI. PAMI 22(1), 38–62 (2000)Google Scholar
- 4.Ganesan, A.: Integration of Surveying and Cadastral GIS: From Field-to-fabric & Land Records-to-fabric. In: Proc. 22nd ESRI User Conference, Redlands, CA, USA, July 7-12 (2002), see, gis.esri.com/library/userconf/proc02/abstracts/a0868.html
- 5.Fletcher, L.A., Kasturi, R.: A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images. PAMI 10(6), 910–918 (1988)Google Scholar
- 6.Tan, C.L., Ng, P.O.: Text Extraction using Pyramid. PR 31(1), 63–72 (1998)Google Scholar
- 7.Velázquez, A.: Localización, Recuperación e Identificación de la Capa de Caracteres contenida en los Planos Cartográficos. Ph.D. Thesis. Centre for Computing Research-IPN. Mexico City, Mexico (2002) (in Spanish)Google Scholar
- 8.Velázquez, A., Levachkine, S.: Text/Graphics Separation in Raster-scanned Color Cartographic Maps. In: Levachkine, S., Serra, J., Egenhofer, M. (eds.) Proc. 2nd Int. Workshop on Semantic Processing of Spatial Data (GEOPRO 2003), Mexico City, Mexico, November 4–5, pp. 34–41 (2003)Google Scholar
- 10.Gelbukh, A.: Syntactic Disambiguation with Weighted Extended Subcategorization Frames. In: Proc. Pacific Association for Computational Linguistics (PACLING 1999), Canada, August 25–28, pp. 244–249 (1999)Google Scholar
- 11.Gelbukh, A.: Exact and approximate prefix search under access locality requirements for morphological analysis and spelling correction. Computación y Sistemas 6(3), 167–182 (2003), see: www.gelbukh.com/CV/Publications/2001/CyS-2001-Morph.htm Google Scholar
- 13.Hirst, G., Budanitsky, A.: Correcting Real-Word Spelling Errors by Restoring Lexical Cohesion. In: Natural Language Engineering (2004) (to appear)Google Scholar
- 14.Levachkine, S., Guzman, A.: Hierarchies as a New Data Type for Qualitative Variables. Journal of Data Knowledge Engineering (DKE) (to appear) Google Scholar
- 15.Gelbukh, A., SangYong, H., Levachkine, S.: Combining Sources of Evidence to Resolve Ambiguities in Toponym Recognition in Cartographic Maps. In: Levachkine, S., Serra, J., Egenhofer, M. (eds.) Proc. 2nd Int. Workshop on Semantic Processing of Spatial Data (GEOPRO 2003), Mexico City, Mexico, November 4–5, pp. 42–51 (2003) ISBN 970-36-0103-0Google Scholar