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- W4387323646 abstract "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." @default.
- W4387323646 created "2023-10-04" @default.
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- W4387323646 date "2023-09-30" @default.
- W4387323646 modified "2023-10-14" @default.
- W4387323646 title "Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation" @default.
- W4387323646 doi "https://doi.org/10.48550/arxiv.2310.00546" @default.
- W4387323646 hasPublicationYear "2023" @default.
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