Seal2Real: 基于扩散模型的提示先验学习用于无监督文档印章数据生成与实现
Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation
AI总结：
Seal2Real通过基于预训练扩散模型的提示先验学习，生成大规模标注的文档印章数据，提升真实数据中印章相关任务的性能。
摘要：
文档处理中的印章相关任务，如印章分割、真实性验证、印章移除和印章下的文本识别，具有重要的商业价值。然而，这些领域的进展受到标注文档印章数据集稀缺的阻碍，而这些数据集对于监督学习至关重要。为了解决这一限制，我们提出了Seal2Real，一种新的生成框架，旨在合成大规模标注的文档印章数据。作为这项工作的组成部分，我们还介绍了Seal-DB，一个包含20,000张标注图像的综合数据集，以支持印章相关研究。Seal2Real引入了一种基于预训练Stable Diffusion模型的提示先验学习架构，有效地将其生成能力转移到印章图像合成的无监督领域。通过生成高度逼真的合成印章图像，Seal2Real显著提高了下游印章相关任务在真实数据上的性能。在Seal-DB数据集上的实验评估证明了所提出框架的有效性和实用性。该数据集可在https://github.com/liuyifan6613/DocBank-Document-Enhancement-Dataset上获取。
英文摘要：
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. The dataset is available at https://github.com/liuyifan6613/DocBank-Document-Enhancement-Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
