DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
This addresses data privacy and adaptation challenges in document processing for automatic systems, though it is incremental as it builds on existing diffusion models.
The authors tackled the problem of document image enhancement for OCR by proposing DECDM, a cycle-consistent diffusion model that eliminates the need for supervised paired data, achieving superior performance on tasks like denoising and shadow removal compared to state-of-the-art methods.
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.