Real-world documents may suffer various forms of degradation, often resulting
in lower accuracy in optical character recognition (OCR) systems. Therefore, a
crucial preprocessing step is essential to eliminate noise while preserving
text and key features of documents. In this paper, we propose NAF-DPM, a novel
generative framework based on a diffusion probabilistic model (DPM) designed to
restore the original quality of degraded documents. While DPMs are recognized
for their high-quality generated images, they are also known for their large
inference time. To mitigate this problem we provide the DPM with an efficient
nonlinear activation-free (NAF) network and we employ as a sampler a fast
solver of ordinary differential equations, which can converge in a few
iterations. To better preserve text characters, we introduce an additional
differentiable module based on convolutional recurrent neural networks,
simulating the behavior of an OCR system during training. Experiments conducted
on various datasets showcase the superiority of our approach, achieving
state-of-the-art performance in terms of pixel-level and perceptual similarity
metrics. Furthermore, the results demonstrate a notable character error
reduction made by OCR systems when transcribing real-world document images
enhanced by our framework. Code and pre-trained models are available at
this https URL

提出一种基于扩散概率模型 (DPM) 的新型生成框架 NAF-DPM，用于恢复被损坏文档的原始质量。通过引入高效无线激活的网络和快速求解常微分方程的采样器，减少了 DPM 的推断时间。实验证明，该方法在像素级和感知相似度度量方面达到了最先进的性能，并且增强了 OCR 系统在转录被我们框架加强的真实文档图像时的字符错误率。