A Generative Approach for Document Enhancement with Small Unpaired Data

Abstract

Shipbuilding drawings, crafted manually before the digital era, are vital for historical reference and technical insight. However, their digital versions, stored as scanned PDFs, often contain significant noise, making them unsuitable for use in modern CAD software like AutoCAD. Traditional denoising techniques struggle with the diverse and intense noise found in these documents, which also does not adhere to standard noise models. In this paper, we propose an innovative generative approach tailored for document enhancement, particularly focusing on shipbuilding drawings. For a small, unpaired dataset of clean and noisy shipbuilding drawing documents, we first learn to generate the noise in the dataset based on a CycleGAN model. We then generate multiple paired clean–noisy image pairs using the clean images in the dataset. Finally, we train a Pix2Pix GAN model with these generated image pairs to enhance shipbuilding drawings. Through empirical evaluation on a small Military Sealift Command (MSC) dataset, we demonstrated the superiority of our method in mitigating noise and preserving essential details, offering an effective solution for the restoration and utilization of historical shipbuilding drawings in contemporary digital environments.