Abstract
The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.
Highlights
We demonstrate that the unbalanced Otsu method modification [20] is better than the classical Otsu method by approximately 3 points in pseudo F-Measure metric
Later in [31], Jian Gong et al suggested the fast computation of the two-dimensional Otsu criterion described in [30] via cumulative summation: with cumulative histogram image computed once, it is feasible within the fixed timeframe to calculate weight of any orthotropic rectangle with its vertex at the origin
We evaluate the accuracy of the methods via one of Document Image Binarization Contest (DIBCO)’s metrics
Summary
The well-known Niblack's method [11] in its classical implementation has two tuning parameters, one of which. They demonstrated that under such assumptions the binarization accuracy could be increased, but the testing dataset was not large (only 13 images) In their other work [28], the authors proposed an approach to accelerate the classical Otsu method. Later in [31], Jian Gong et al suggested the fast computation of the two-dimensional Otsu criterion described in [30] via cumulative summation: with cumulative histogram image computed once, it is feasible within the fixed timeframe to calculate weight (sum of all histogram pixels) of any orthotropic rectangle (which was used as the separating surface) with its vertex at the origin This is true for different statistics (mean, variance, and etc.). Otsu threshold is used as an input parameter during the pre-processing [39, 40], for the binarization of the “simple” images [15, 41 – 43], and for global threshold value computation [44]
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