An optimization for binarization methods by removing binary artifacts
Pattern Recognition Letters
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This paper extends the transition method for binarization based on transition pixels, a generalization of edge pixels. This method originally computes transition thresholds using the quantile thresholding algorithm, that has a critical parameter. We achieved an automatic version of the transition method by computing the transition thresholds with the Rosin's algorithm. We experimentally tested four variants of the transition method combining the density and cumulative distribution functions of transition values, with gray-intensity thresholds based on the normal and lognormal density functions. The results of our experiments show that these unsupervised methods yields superior binarization compared with top-ranked algorithms.