  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.
