  Inspired by the classic Sauvola local image thresholding approach, we
systematically study it from the deep neural network (DNN) perspective and
propose a new solution called SauvolaNet for degraded document binarization
(DDB). It is composed of three explainable modules, namely, Multi-Window
Sauvola (MWS), Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold
(AST). The MWS module honestly reflects the classic Sauvola but with trainable
parameters and multi-window settings. The PWA module estimates the preferred
window sizes for each pixel location. The AST module further consolidates the
outputs from MWS and PWA and predicts the final adaptive threshold for each
pixel location. As a result, SauvolaNet becomes end-to-end trainable and
significantly reduces the number of required network parameters to 40K -- it is
only 1\% of MobileNetV2. In the meantime, it achieves the State-of-The-Art
(SoTA) performance for the DDB task -- SauvolaNet is at least comparable to, if
not better than, SoTA binarization solutions in our extensive studies on the 13
public document binarization datasets. Our source code is available at
https://github.com/Leedeng/SauvolaNet.
