# MinerU-Popo **Repository Path**: mysd/MinerU-Popo ## Basic Information - **Project Name**: MinerU-Popo - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-08-20 - **Last Updated**: 2026-08-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

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![image](./figures/intro.png) ## ✨ Introduction **MinerU-Popo** is a lightweight and universal framework for POst-Processing OCR outputs, bridging the gap between page-level OCR parsing and document-level semantic structure. It constructs document tree structures with a 4B post-processing model that performs four subtasks: table truncation analysis, text truncation analysis, title hierarchy analysis, and image-text association analysis. We handle the challenges of cross-page geometric discontinuity, redundant document parsing, and scalability to long documents via: - **Task-Oriented Data Engine**: Generate representative training data and simplify the task-specific input. - **Dynamic Chunking and Synchronization**: Process long document by dynamic chunks and reduce deviations across chunks to preserve global consistency. - **Document Enrichment**: Structurally construct a tree, semantically generate summaries and split long-section nodes. ![image](./figures/overview.png) ## 📊 Performance ### Better Hierarchy (TEDS) after Post-Processing **Basic OCR** | **Before** | **After** :---:|:---:|:---:| MinerU | 53.7 | **90.6** | MonkeyOCR | 48.9 | **87.4** | Dolphin | 60.4 | **83.5** | PaddleOCR | 59.3 | **82.6** | GLM-OCR | 53.5 | **81.8** | ### Advantages Compared to Directly Using Pre-trained Model **Model** | **TEDS** | **Doc/s** :---:|:---:|:---:| MinerU-Popo | **90.6** | **0.37** | Qwen3-VL-2B | 21.2 | 0.22 | Qwen3-VL-4B | 56.5 | 0.20 | Qwen3-VL-8B | 65.9 | 0.16 | Qwen3-VL-32B | 78.0 | 0.04 | ### Benefits for Downstream Retrieval and Analysis (Acc on ViDoRe V3) **Method** | **C.S.** | **Fin.** | **H.R.** | **Ind.** | **Phar.** :---:|:---:|:---:|:---:|:---:|:---:| MinerU-Popo | **84.4** | 49.5 | **66.8** | 58.7 | **71.6** Raw RAG | 82.3 | 48.7 | 63.2 | **60.4** | 64.4 Visual RAG | 80.7 | **58.4** | 64.8 | 59.7 | 67.6 ## ⚙️ Setup ### Prepare Environment #### Install from Source ```bash conda create -n popo python=3.10 conda activate popo pip install -r requirements.txt ``` #### Install from Docker Image ```bash docker run -it --rm --gpus=all --ipc=host --network=host dockerrr8277/mineru-popo-vllm:latest ``` ### Download Model Download the MinerU-Popo post-processing model: ```bash hf download DreamEternal/MinerU-Popo --local-dir models/Mineru-Popo ``` - [MinerU-Popo](https://huggingface.co/DreamEternal/MinerU-Popo) ### Model Configuration In the [Configuration](./post_processing/model_utils.py), for transformer inference, edit the environment `POPO_MODEL_PATH`. For vllm inference, edit the `url` and `key` in function `popo_generate`. For enrichment and question answering, further edit the `url` and `key` in `qwen_generate` and `gpt_generate`. ## 💻 Usage The post-processing pipeline takes page-level parsing results from OCR/layout systems, normalizes them into a unified schema, runs MinerU-Popo inference, and finally builds document trees. ### Step 1: Prepare OCR/Layout Outputs Run your preferred page-level parser first, such as MinerU, MonkeyOCR, Dolphin, PaddleOCR-VL, or GLM-OCR. Place each model's output under: ```text post-process// ``` For example: ```text post-process/mineru/ post-process/monkeyocr/ post-process/PaddleOCR-VL-1.5/ post-process/dolphin/ post-process/glm-ocr/ ``` ### Step 2: Normalize Labels Convert raw model-specific labels and bounding boxes into the unified MinerU-Popo input format: ```bash bash scripts/run_label_normalization.sh ``` The normalized outputs are written to: ```text outputs/label_normalization// ``` ### Step 3: Run MinerU-Popo Inference Run MinerU-Popo on the normalized labels: ```bash bash scripts/run_inference.sh ``` The inference outputs are written to: ```text outputs/inference// ``` ### Step 4: Build Document Trees Build structured document trees from the inference outputs: ```bash bash scripts/build_tree.sh ``` The final tree outputs and text previews are written to: ```text outputs/build_tree// outputs/build_tree_txt// ``` Example tree outputs are provided in: ```text output_cases/ ``` ## 🙏 Acknowledgements - [MinerU](https://github.com/opendatalab/MinerU) and other OCR system (MonkeyOCR, Dolphin, PaddleOCR, GLM-OCR) for page-level parsing. - [ViDoRe V3](https://huggingface.co/datasets/vidore/vidore-benchmark-v3) and [MMDA](https://huggingface.co/datasets/DreamEternal/MMDA_Bench) as benchmarks. ## 📚 Citation If you find this project useful for your research, please consider giving us a star and citing our paper: ```bibtex @article{xu2026mineru, title={MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing}, author={Xu, Bangrui and Miao, Ziyang and Zhou, Xuanhe and Lin, Yiming and Tang, Zirui and Zhao, Xiaomeng and Wu, Fan and Tan, Cheng and Wang, Bin and He, Conghui}, journal={arXiv preprint arXiv:2605.24973}, year={2026} } ``` ## 📄 License This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.