  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.
