Open AccessMorphological Background Detection and Illumination Normalization of Text Image with Poor Lighting
Author(s) -
Guocheng Wang,
Yiwen Wang,
Hui Li,
Xuanqi Chen,
Haitao Lü,
Youjie Ma,
Chun Wei Peng,
Yijun Wang,
Tang Linyao
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0110991
Subject(s) - normalization (sociology) , artificial intelligence , computer vision , computer science , image processing , closing (real estate) , top hat transform , pattern recognition (psychology) , image (mathematics) , digital image processing , sociology , anthropology , political science , law
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.