| license: mit | |
| library_name: pytorch | |
| pipeline_tag: image-to-image | |
| tags: | |
| - document-image-restoration | |
| - dewarping | |
| - deshadowing | |
| - deblurring | |
| - binarization | |
| - appearance-enhancement | |
| - doctra | |
| model-index: | |
| - name: DocRes (Main Weights, docres.pkl) | |
| results: [] | |
| # DocRes Main Weights (docres.pkl) | |
| These are the official **DocRes** (CVPR 2024) main weights (`docres.pkl`), rehosted for use in the [Doctra](https://github.com/your-username/doctra) library. | |
| --- | |
| ## 📖 Source | |
| - Original repository: [ZZZHANG-jx/DocRes](https://github.com/ZZZHANG-jx/DocRes) | |
| - Paper: *DocRes: Dynamic Task-Specific Prompt for Generalist Document Image Restoration* (CVPR 2024) | |
| --- | |
| ## ⚖️ License | |
| MIT License (see LICENSE file). | |
| Weights are redistributed under the same terms, with attribution to the original authors. | |
| --- | |
| ## ✅ Intended Use | |
| The `docres.pkl` weights are used with the DocRes model backbone to perform generalist document image restoration tasks, including: | |
| - 📐 Dewarping | |
| - 🌗 Deshadowing | |
| - ✨ Appearance enhancement (illumination cleanup) | |
| - 🔄 Deblurring | |
| - ⚫ Binarization | |
| These weights are integrated into the **Doctra** library to improve preprocessing and restoration of scanned or photographed documents. | |
| --- | |
| ## ⚠️ Limitations | |
| - Performance may not always exceed highly specialized single-task models. | |
| - Trained on specific datasets (see [source repo](https://github.com/ZZZHANG-jx/DocRes) for details). | |
| - Not intended for non-document natural images. | |
| --- |