A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking text in mixed-mode documents
DOCPROCS '88 Proceedings of the ACM conference on Document processing systems
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Verification-Based Approach for Automated Text and Feature Extraction from Raster-Scanned Maps
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Text/Graphics Separation in Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Generalized Morphological Operators Applied to Map-Analysis
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Cooperative Text and Line-Art Extraction from a Topographic Map
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Automatically and accurately conflating orthoimagery and street maps
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Automatically identifying and georeferencing street maps on the web
Proceedings of the 2005 workshop on Geographic information retrieval
Automatic extraction of road intersections from raster maps
Proceedings of the 13th annual ACM international workshop on Geographic information systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Automatically Conflating Road Vector Data with Orthoimagery
Geoinformatica
Identifying Maps on the World Wide Web
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Classification of raster maps for automatic feature extraction
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Strabo: a system for extracting road vector data from raster maps
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Extracting road vector data from raster maps
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Segmentation of colour layers in historical maps based on hierarchical colour sampling
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Exploiting semantics of web services for geospatial data fusion
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Semantics and Ontologies
Efficient and robust graphics recognition from historical maps
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.