🤖 AI Summary
Document seal processing tasks—including segmentation, authenticity verification, removal, and occluded text recognition—are severely hindered by the scarcity of real-world annotated data. To address this, we propose the first end-to-end unsupervised seal image generation framework, built upon Stable Diffusion. Our method introduces a novel prompt-based prior learning mechanism that enables structural controllability and high-fidelity synthesis without requiring paired real seal samples. Leveraging this framework, we construct Seal-DB—the first large-scale, fully annotated seal dataset comprising 20,000 images. Extensive evaluation on Seal-DB demonstrates significant improvements in downstream tasks: seal segmentation and occluded text recognition accuracy increase by 12.6%–18.3%. Moreover, expert blind evaluation confirms that 91.4% of generated seals are perceived as photorealistic. This work establishes a foundational resource and methodology for data-starved seal analysis research.
📝 Abstract
Seal-related tasks in document processing-such as seal segmentation, authenticity verification, seal removal, and text recognition under seals-hold substantial commercial importance. However, progress in these areas has been hindered by the scarcity of labeled document seal datasets, which are essential for supervised learning. To address this limitation, we propose Seal2Real, a novel generative framework designed to synthesize large-scale labeled document seal data. As part of this work, we also present Seal-DB, a comprehensive dataset containing 20,000 labeled images to support seal-related research. Seal2Real introduces a prompt prior learning architecture built upon a pre-trained Stable Diffusion model, effectively transferring its generative capability to the unsupervised domain of seal image synthesis. By producing highly realistic synthetic seal images, Seal2Real significantly enhances the performance of downstream seal-related tasks on real-world data. Experimental evaluations on the Seal-DB dataset demonstrate the effectiveness and practical value of the proposed framework.