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Neuro semantic thresholding using OCR software for high precision OCR applications
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DOI：10.1016/j.imavis.2009.09.011.png)
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Abstract
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This paper describes a novel approach to binarization techniques. It presents a way of obtaining a threshold that depends both on the image and the final application using a semantic description of the histogram and a neural network. The intended applications of this technique are high precision OCR algorithms over a limited number of document types. The input image histogram is smoothed and its derivative is found. Using a polygonal Version of the derivative and the smoothed histogram, a new description of the histogram is calculated. Using this description and a training set, a general neural network is capable of obtaining an Optimum threshold for our application. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Thresholding
Binarization
GRNN
Semantic description
OCR
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