# LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach

arXiv: https://arxiv.org/abs/2406.08610 · HTML: https://mlsanity.com/paper/2406.08610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://mlsanity.com/paper/1901.09221/) (2019)
- [Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition](https://mlsanity.com/paper/2005.13044/) (2020)
- [Single Image Reflection Removal through Cascaded Refinement](https://mlsanity.com/paper/1911.06634/) (2019)
- [DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement](https://mlsanity.com/paper/2010.08764/) (2020)
- [WDNet: Watermark-Decomposition Network for Visible Watermark Removal](https://mlsanity.com/paper/2012.07616/) (2020)
- [High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net](https://mlsanity.com/paper/2308.14221/) (2023)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [DocEnTr: An End-to-End Document Image Enhancement Transformer](https://mlsanity.com/paper/2201.10252/) (2022)
- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [Cross-Domain Document Object Detection: Benchmark Suite and Method](https://mlsanity.com/paper/2003.13197/) (2020)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer](https://mlsanity.com/paper/2201.11438/) (2022)
- [Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement](https://mlsanity.com/paper/2203.04814/) (2022)
- [SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation](https://mlsanity.com/paper/2305.04609/) (2023)
- [Intrinsic Decomposition of Document Images In-the-Wild](https://mlsanity.com/paper/2011.14447/) (2020)
- [Learning Foreground-Background Segmentation from Improved Layered GANs](https://mlsanity.com/paper/2104.00483/) (2021)

## Built on by (most cited first, 1 of 1)

- [DocRevive: A Unified Pipeline for Document Text Restoration](https://mlsanity.com/paper/2604.10077/) (2026)

## Abstract (arXiv)

The rapid evolution of intelligent document processing systems demands robust solutions that adapt to diverse domains without extensive retraining. Traditional methods often falter with variable document types, leading to poor performance. To overcome these limitations, this paper introduces a text-graphic layer separation approach that enhances domain adaptability in document image restoration (DIR) systems. We propose LayeredDoc, which utilizes two layers of information: the first targets coarse-grained graphic components, while the second refines machine-printed textual content. This hierarchical DIR framework dynamically adjusts to the characteristics of the input document, facilitating effective domain adaptation. We evaluated our approach both qualitatively and quantitatively using a new real-world dataset, LayeredDocDB, developed for this study. Initially trained on a synthetically generated dataset, our model demonstrates strong generalization capabilities for the DIR task, offering a promising solution for handling variability in real-world data. Our code is accessible on GitHub.