TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916645074632704 |
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| author | Lu, Wanglong Su, Lingming Zheng, Jingjing de Melo, Vinícius Veloso Shoeleh, Farzaneh Hawkin, John Tricco, Terrence Zhao, Hanli Jiang, Xianta |
| author_facet | Lu, Wanglong Su, Lingming Zheng, Jingjing de Melo, Vinícius Veloso Shoeleh, Farzaneh Hawkin, John Tricco, Terrence Zhao, Hanli Jiang, Xianta |
| contents | Digital versions of real-world text documents often suffer from issues like environmental corrosion of the original document, low-quality scanning, or human interference. Existing document restoration and inpainting methods typically struggle with generalizing to unseen document styles and handling high-resolution images. To address these challenges, we introduce TextDoctor, a novel unified document image inpainting method. Inspired by human reading behavior, TextDoctor restores fundamental text elements from patches and then applies diffusion models to entire document images instead of training models on specific document types. To handle varying text sizes and avoid out-of-memory issues, common in high-resolution documents, we propose using structure pyramid prediction and patch pyramid diffusion models. These techniques leverage multiscale inputs and pyramid patches to enhance the quality of inpainting both globally and locally. Extensive qualitative and quantitative experiments on seven public datasets validated that TextDoctor outperforms state-of-the-art methods in restoring various types of high-resolution document images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models Lu, Wanglong Su, Lingming Zheng, Jingjing de Melo, Vinícius Veloso Shoeleh, Farzaneh Hawkin, John Tricco, Terrence Zhao, Hanli Jiang, Xianta Computer Vision and Pattern Recognition Artificial Intelligence 68U10 I.4.3; I.4.4; I.4.5; I.4.9 Digital versions of real-world text documents often suffer from issues like environmental corrosion of the original document, low-quality scanning, or human interference. Existing document restoration and inpainting methods typically struggle with generalizing to unseen document styles and handling high-resolution images. To address these challenges, we introduce TextDoctor, a novel unified document image inpainting method. Inspired by human reading behavior, TextDoctor restores fundamental text elements from patches and then applies diffusion models to entire document images instead of training models on specific document types. To handle varying text sizes and avoid out-of-memory issues, common in high-resolution documents, we propose using structure pyramid prediction and patch pyramid diffusion models. These techniques leverage multiscale inputs and pyramid patches to enhance the quality of inpainting both globally and locally. Extensive qualitative and quantitative experiments on seven public datasets validated that TextDoctor outperforms state-of-the-art methods in restoring various types of high-resolution document images. |
| title | TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68U10 I.4.3; I.4.4; I.4.5; I.4.9 |
| url | https://arxiv.org/abs/2503.04021 |