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Morphological Background Detection and Illumination Normalization of Text Image with Poor Lighting

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Listed:
  • Guocheng Wang
  • Yiwen Wang
  • Hui Li
  • Xuanqi Chen
  • Haitao Lu
  • Yanpeng Ma
  • Chun Peng
  • Yijun Wang
  • Linyao Tang

Abstract

In this paper, some morphological transformations are used to detect the unevenly illuminated background of text images characterized by poor lighting, and to acquire illumination normalized result. Based on morphologic Top-Hat transform, the uneven illumination normalization algorithm has been carried out, and typically verified by three procedures. The first procedure employs the information from opening based Top-Hat operator, which is a classical method. In order to optimize and perfect the classical Top-Hat transform, the second procedure, featuring the definition of multi direction illumination notion, utilizes opening by reconstruction and closing by reconstruction based on multi direction structuring elements. Finally, multi direction images are merged to the final even illumination image. The performance of the proposed algorithm is illustrated and verified through the processing of different ideal synthetic and camera collected images, with backgrounds characterized by poor lighting conditions.

Suggested Citation

  • Guocheng Wang & Yiwen Wang & Hui Li & Xuanqi Chen & Haitao Lu & Yanpeng Ma & Chun Peng & Yijun Wang & Linyao Tang, 2014. "Morphological Background Detection and Illumination Normalization of Text Image with Poor Lighting," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-22, November.
    • Handle: RePEc:plo:pone00:0110991
    DOI: 10.1371/journal.pone.0110991
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    References listed on IDEAS

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    1. Cong Yao & Xin Zhang & Xiang Bai & Wenyu Liu & Yi Ma & Zhuowen Tu, 2013. "Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
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