# Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

arXiv: https://arxiv.org/abs/1911.10714 · HTML: https://mlsanity.com/paper/1911.10714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2303.16513/) (2023)
- [Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity](https://mlsanity.com/paper/2409.05466/) (2024)
- [Learning Position-Aware Implicit Neural Network for Real-World Face Inpainting](https://mlsanity.com/paper/2401.10537/) (2024)
- [MatteViT: High-Frequency-Aware Document Shadow Removal with Shadow Matte Guidance](https://mlsanity.com/paper/2512.08789/) (2025)

## Abstract (arXiv)

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.