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Finding the Best-Fit Bounding-Boxes

  • Bo Yuan
  • Leong Keong Kwoh
  • Chew Lim Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

The bounding-box of a geometric shape in 2D is the rectangle with the smallest area in a given orientation (usually upright) that complete contains the shape. The best-fit bounding-box is the smallest bounding-box among all the possible orientations for the same shape. In the context of document image analysis, the shapes can be characters (individual components) or paragraphs (component groups). This paper presents a search algorithm for the best-fit bounding-boxes of the textual component groups, whose shape are customarily rectangular in almost all languages. One of the applications of the best-fit bounding-boxes is the skew estimation from the text blocks in document images. This approach is capable of multi-skew estimation and location, as well as being able to process documents with sparse text regions. The University of Washington English Document Image Database (UW-I) is used to verify the skew estimation method directly and the proposed best-fit bounding-boxes algorithm indirectly.

Keywords

Document Image Component Group Text Block Fiducial Point Orientation Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Yuan
    • 1
  • Leong Keong Kwoh
    • 1
  • Chew Lim Tan
    • 2
  1. 1.Centre for Remote Imaging, Sensing and ProcessingNational University of SingaporeSingapore
  2. 2.Department of Computer Science, School of ComputingNational University of SingaporeSingapore

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