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Proceedings Paper

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images
Author(s): Deepak Kumar; M. N. Anil Prasad; A. G. Ramakrishnan
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Paper Abstract

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. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Paper Details

Date Published: 4 February 2013
PDF: 10 pages
Proc. SPIE 8658, Document Recognition and Retrieval XX, 865806 (4 February 2013); doi: 10.1117/12.2008519
Show Author Affiliations
Deepak Kumar, Indian Institute of Science (India)
M. N. Anil Prasad, Indian Institute of Science (India)
A. G. Ramakrishnan, Indian Institute of Science (India)

Published in SPIE Proceedings Vol. 8658:
Document Recognition and Retrieval XX
Richard Zanibbi; Bertrand Coüasnon, Editor(s)

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