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.

我们提出了一种通用的直方图阈值技术（GHT），它可以通过高斯混合的近似最大后验估计来进行工作，并展示了它的实用性和有效性。该技术可以涵盖三种经典的阈值技术算法：Otsu方法、最小误差阈值法（MET）和加权百分位数阈值法。GHT可以在这三种算法之间进行连续的插值，从而显著提高了阈值精度并提供了更清晰的阈值解释，同时提供了解释直方图bin width调整的公共实践，总而言之，在手写文件图像二值化的最新挑战中，GHT的表现超过或匹配所有算法，包括经过训练产生像素级二值化的深度神经网络，可用12行代码或对Otsu方法或MET进行微不足道的修改。

一种Otsu方法和最小误差阈值的推广