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Efficient and Robust Graphics Recognition from Historical Maps

  • Yao-Yi Chiang
  • Stefan Leyk
  • Craig A. Knoblock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7423)

Abstract

Historical maps contain rich cartographic information, such as road networks, but this information is “locked” in images and inaccessible to a geographic information system (GIS). Manual map digitization requires intensive user effort and cannot handle a large number of maps. Previous approaches for automatic map processing generally require expert knowledge in order to fine-tune parameters of the applied graphics recognition techniques and thus are not readily usable for non-expert users. This paper presents an efficient and effective graphics recognition technique that employs interactive user intervention procedures for processing historical raster maps with limited graphical quality. The interactive procedures are performed on color-segmented preprocessing results and are based on straightforward user training processes, which minimize the required user effort for map digitization. This graphics recognition technique eliminates the need for expert users in digitizing map images and provides opportunities to derive unique data for spatiotemporal research by facilitating time-consuming map digitization efforts. The described technique generated accurate road vector data from a historical map image and reduced the time for manual map digitization by 38%.

Keywords

Color image segmentation road vectorization historical raster maps image cleaning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yao-Yi Chiang
    • 1
  • Stefan Leyk
    • 2
  • Craig A. Knoblock
    • 3
  1. 1.Information Sciences Institute and Spatial Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  2. 2.Department of GeographyUniversity of ColoradoBoulderUSA
  3. 3.Department of Computer Science and Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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