# Analysis of PDE-based binarization model for degraded document images

arXiv: https://arxiv.org/abs/2111.05471 · HTML: https://mlsanity.com/paper/2111.05471/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 10 Nov 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [PDNet: Semantic Segmentation integrated with a Primal-Dual Network for Document binarization](https://mlsanity.com/paper/1801.08694/) (2018)

## Abstract (arXiv)

This report presents the results of a PDE-based binarization model for degraded document images. The model utilizes an edge and binary source term in its formulation. Results indicate effectiveness for document images with bleed-through and faded text and stains to a lesser extent.