Optimal Line and Arc Detection on Run-Length Representations

  • Daniel Keysers
  • Thomas M. Breuel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3926)


The robust detection of lines and arcs in scanned documents or technical drawings is an important problem in document image understanding. We present a new solution to this problem that works directly on run-length encoded data. The method finds globally optimal solutions to parameterized thick line and arc models. Line thickness is part of the model and directly used during the matching process. Unlike previous approaches, it does not require any thinning or other preprocessing steps, no computation of the line adjacency graphs, and no heuristics. Furthermore, the only search-related parameter that needs to be specified is the desired numerical accuracy of the solution. The method is based on a branch-and-bound approach for the globally optimal detection of these geometric primitives using runs of black pixels in a bi-level image. We present qualitative and quantitative results of the algorithm on images used in the 2003 and 2005 GREC arc segmentation contests.


Graphics Recognition Line Drawings Technical Drawings Branch-and-Bound algorithms 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Keysers
    • 1
    • 2
  • Thomas M. Breuel
    • 1
    • 2
  1. 1.Image Understanding and Pattern Recognition Research Group, German Research Center for Artificial Intelligence (DFKI GmbH)Germany
  2. 2.University of KaiserslauternKaiserslauternGermany

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