  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.
