Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Investigates the nature and performance of the native integral ratio (NIR) technique for separating the foreground from the background in a grey-scale handwriting image. The technique separates the pixels into three groups: background, foreground and a fuzzy group of pixels which may be either foreground or background. The NIR has previously been shown to perform well in qualitative experiments by comparing of the resulting thresholded images with other thresholding techniques. In this paper, the properties of the NIR technique are analysed on hypothetical (Gaussian) distributions. The technique is found to be robust to the variation of the distribution strength and variance, and to the presence of strong uniform noise.