In document processing, seal-related tasks have very large commercial applications, such as seal segmentation, seal authenticity discrimination, seal removal, and text recognition under seals. However, these seal-related tasks are highly dependent on labelled document seal datasets, resulting in very little work on these tasks. To address the lack of labelled datasets for these seal-related tasks, we propose Seal2Real, a generative method that generates a large amount of labelled document seal data, and construct a Seal-DB dataset containing 20K images with labels. In Seal2Real, we propose a prompt prior learning architecture based on a pre-trained Stable Diffusion Model that migrates the prior generative power of to our seal generation task with unsupervised training. The realistic seal generation capability greatly facilitates the performance of downstream seal-related tasks on real data. Experimental results on the Seal-DB dataset demonstrate the effectiveness of Seal2Real.

通过提出一个生成方法Seal2Real，我们可以生成大量带有标签的文档印章数据，并构建一个包含20K张带标签图像的Seal-DB 数据集，该方法极大地促进了后续的印章相关任务在真实数据上的表现。

Seal2Real：基于扩散模型的无监督文档印章数据生成和实现的启示先验学习