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A New Text Detection Algorithm for Content-Oriented Line Drawing Image Retrieval

  • Zhenyu Zhang
  • Tong Lu
  • Feng Su
  • Ruoyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

Content retrieval of scanned line drawing images is a difficult problem, especially from real-life large scale databases. Existing algorithms don’t work well due to their low efficiency by first recognizing various types of graphical primitives and then content-oriented texts. A new method for directly detecting texts from line drawing images is proposed in this paper. We first decompose a drawing image into a set of Local Consecutive Segments (LCSs). A LCS is defined as a minimum meaningful structural unit to imitate a stroke during human-drawing process. Next, we identify candidate character LCSs by statistical analysis and merge them into character LCS blocks by geometrical analysis. Finally, Hough transforms are applied to calculate the orientations of character LCS blocks and generate candidate strings. Experimental results show that our algorithm can well detect strings in any orientation. Our method is robust to text-graphic touching, scanning degradation and drawing noises, providing an efficient approach for content retrieval of document images.

Keywords

text detection content-oriented line drawings image retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zhenyu Zhang
    • 1
  • Tong Lu
    • 1
    • 2
  • Feng Su
    • 1
  • Ruoyu Yang
    • 1
  1. 1.State Key Lab. for Novel Software TechnologyNanjing UniversityChina
  2. 2.Jiangyin Institute of Information Technology of Nanjing UniversityChina

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