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Text Localization and Recognition in Complex Scenes Using Local Features

  • Qi Zheng
  • Kai Chen
  • Yi Zhou
  • Congcong Gu
  • Haibing Guan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

We describe an approach using local features to resolve problems in text localization and recognition in complex scenes. Low image quality, complex background and variations of text make these problems challenging. Our approach includes the following stages: (1) Template images are generated automatically; (2) SIFT features are extracted and matched to template images; (3) Multiple single-character-areas are located using segmentation algorithm based upon multiple-size sliding sub-windows; (4) An voting and geometric verification algorithm is used to identify final results. This framework thus is essentially simple by skipping many steps, such as normalization, binarization and OCR, which are required in previous methods. Moreover, this framework is robust as only SIFT feature is used. We evaluated our method using 200,000+ images in 3 scripts (Chinese, Japanese and Korean). We obtained average single-character success rate of 77.3% (highest 94.1%), average multiple-character success rate of 63.9% (highest 89.6%).

Keywords

Local Feature Chinese Character Query Image Maximal Clique Natural Scene 
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 2011

Authors and Affiliations

  • Qi Zheng
    • 1
  • Kai Chen
    • 1
  • Yi Zhou
    • 1
  • Congcong Gu
    • 1
  • Haibing Guan
    • 2
  1. 1.School of Information Security EngineeringShanghai Jiao Tong UniversityChina
  2. 2.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityChina

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