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Abstract: In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO׳s benchmarks. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a mechanism for systematic comparison of the efficacy of unsupervised evaluation methods for parameter selection of binarization algorithms in optical character recognition (OCR). We also analyze these measures statistically and ascertain whether a measure is suitable or not to assess a binarization method. The comparison process is streamlined in several steps. Given an unsupervised measure and a binarization algorithm we: (i) find the best parameter combination for the algorithm in terms of the measure, (ii) use the best binarization of an image on an OCR, and (iii) evaluate the accuracy of the characters detected. We also propose a new unsupervised measure and a statistical test to compare measures based on an intuitive triad of possible results: better, worse or comparable performance. The comparison method and statistical tests can be easily generalized for new measures, binarization algorithms and even other accuracy-driven tasks in image processing. Finally, we perform an extensive comparison of several well known measures, binarization algorithms and OCRs, and use it to show the strengths of the WV measure. [ABSTRACT FROM AUTHOR]

Abstract: The transition method for image binarization is based on the concept of t-transition pixels, a generalization of edge pixels, and t-transition sets. We introduce a novel unsupervised thresholding for unimodal histograms to estimate the transition sets. We also present dilation and incidence transition operators to refine the transition set. Afterward, we propose the simple edge transition operator for detecting edges. Our experiments show that the new approach increases the effectiveness of OCR applications outperforming several top-ranked binarization algorithms. [Copyright &y& Elsevier]

Abstract: This paper introduces a novel binarization method based on the concept of transition pixel, a generalization of edge pixels. Such pixels are characterized by extreme transition values computed using pixel-intensity differences in a small neighborhood. We show how to adjust the threshold of several binary threshold methods which compute gray-intensity thresholds, using the gray-intensity mean and variance of the pixels in the transition set. Our experiments show that the new approach yields segmentation performance superior to several with current state-of-the-art binarization algorithms. [Copyright &y& Elsevier]