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In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature.

Added on December 18, 2017


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  • Contributed by : Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Deepak Kumar,M. N. Anil Prasad,A. G. Ramakrishnan
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