# A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

arXiv: https://arxiv.org/abs/2007.09824 · HTML: https://mlsanity.com/paper/2007.09824/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Fourier Document Restoration for Robust Document Dewarping and Recognition](https://mlsanity.com/paper/2203.09910/) (2022)
- [Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild](https://mlsanity.com/paper/2207.11515/) (2022)
- [RectiNet-v2: A stacked network architecture for document image dewarping](https://mlsanity.com/paper/2102.01120/) (2021)
- [Efficient Document Image Dewarping via Hybrid Deep Learning and Cubic Polynomial Geometry Restoration](https://mlsanity.com/paper/2501.03145/) (2025)
- [ForCenNet: Foreground-Centric Network for Document Image Rectification](https://mlsanity.com/paper/2507.19804/) (2025)
- [Unfolder: Fast localization and image rectification of a document with a crease from folding in half](https://mlsanity.com/paper/2312.00467/) (2023)

## Abstract (arXiv)

Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUNet dataset after being trained on just 8 percent of the data used in previous methods.