Abstract
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.
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© 1996 Springer-Verlag Berlin Heidelberg
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Myers, G.K., Mulgaonkar, P.G., Chen, CH., DeCurtins, J.L., Chen, E. (1996). Verification-based approach for automated text and feature extraction from raster-scanned maps. In: Kasturi, R., Tombre, K. (eds) Graphics Recognition Methods and Applications. GREC 1995. Lecture Notes in Computer Science, vol 1072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61226-2_16
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DOI: https://doi.org/10.1007/3-540-61226-2_16
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