The performance of optical character recognition (OCR) heavily relies on
document image quality, which is crucial for automatic document processing and
document intelligence. However, most existing document enhancement methods
require supervised data pairs, which raises concerns about data separation and
privacy protection, and makes it challenging to adapt these methods to new
domain pairs. To address these issues, we propose DECDM, an end-to-end
document-level image translation method inspired by recent advances in
diffusion models. Our method overcomes the limitations of paired training by
independently training the source (noisy input) and target (clean output)
models, making it possible to apply domain-specific diffusion models to other
pairs. DECDM trains on one dataset at a time, eliminating the need to scan both
datasets concurrently, and effectively preserving data privacy from the source
or target domain. We also introduce simple data augmentation strategies to
improve character-glyph conservation during translation. We compare DECDM with
state-of-the-art methods on multiple synthetic data and benchmark datasets,
such as document denoising and {\color{black}shadow} removal, and demonstrate
the superiority of performance quantitatively and qualitatively.

基于扩散模型的光学字符识别（OCR）在自动文档处理和文档智能方面对文档图像质量非常依赖，研究提出了一种新的端到端文档级图像转换方法 DECDM 来解决现有方法的数据分离和隐私保护问题，该方法通过独立训练源和目标模型，消除了配对训练的限制，提高了性能的数量和质量的比较。