A Generalization of Otsu's Method and Minimum Error Thresholding

TL;DR

The paper introduces Generalized Histogram Thresholding (GHT), a fast and effective method that unifies and extends classic thresholding techniques, improving accuracy in image binarization tasks.

Contribution

GHT generalizes Otsu's method, MET, and weighted percentile thresholding, enabling continuous interpolation and improved thresholding accuracy.

Findings

GHT outperforms existing methods on handwritten document binarization.

GHT can be implemented in a few lines of code.

GHT unifies and extends classic thresholding techniques.

Abstract

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.