# SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

arXiv: https://arxiv.org/abs/2105.05521 · HTML: https://mlsanity.com/paper/2105.05521/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 4 of 4)

- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement](https://mlsanity.com/paper/2010.08764/) (2020)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [Learning Document Image Binarization from Data](https://mlsanity.com/paper/1505.00529/) (2015)

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep learning based Document Image Enhancement](https://mlsanity.com/paper/2112.02719/) (2021)
- [DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization](https://mlsanity.com/paper/2312.03568/) (2023)
- [A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement](https://mlsanity.com/paper/2312.03946/) (2023)
- [DECDM: Document Enhancement using Cycle-Consistent Diffusion Models](https://mlsanity.com/paper/2311.09625/) (2023)
- [A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches](https://mlsanity.com/paper/2401.11831/) (2024)
- [HyperBank: A Differentiable Bank of Classical Priors for Few-Shot Spheroid Microscopy Segmentation](https://mlsanity.com/paper/2607.10684/) (2026)
- [LineCounter: Learning Handwritten Text Line Segmentation by Counting](https://mlsanity.com/paper/2105.11307/) (2021)
- [ERN-Net : Evolving Reason Node-Net for Document Binarization](https://mlsanity.com/paper/2606.11710/) (2026)

## Abstract (arXiv)

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.