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@@ -33,3 +33,26 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/formula_detection/textbook.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/formula_detection/wikipedia_sce.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/exam_paper.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/financial_report.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/fuzzy_scan.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/paper.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/slides.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/layout_detection/watermark.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/ocr/ocr_001.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/ocr/ocr_002.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/PDFs/DDPM.pdf filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/PDFs/UniMERNet.pdf filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/demo/table_parsing/table_001.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/readme/layout_example.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/readme/mfd_example.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/readme/pipeline.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/readme/table_expamle.png filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/assets/readme/unimernet_result.jpg filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/docs/zh_cn/_build/html/_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.ttf filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/docs/zh_cn/_build/html/_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2 filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/docs/zh_cn/_build/html/_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.ttf filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/docs/zh_cn/_build/html/_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2 filter=lfs diff=lfs merge=lfs -text
+PDF-Extract-Kit/project/pdf2markdown/demo.png filter=lfs diff=lfs merge=lfs -text

PDF-Extract-Kit/.gitignore

@@ -0,0 +1,22 @@
+*.ipynb*
+*.ipynb
+
+# local data
+outputs/*
+data/*
+temp*
+test*
+
+# python
+.ipynb_checkpoints
+*.ipynb
+**/__pycache__/
+
+# logs
+*.log
+*.out
+
+models/*
+
+# Sphinx documentation
+docs/*/_build/

PDF-Extract-Kit/.readthedocs.yaml

@@ -0,0 +1,16 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: requirements/docs.txt
+
+sphinx:
+  configuration: docs/zh_cn/conf.py

PDF-Extract-Kit/.vscode/launch.json

@@ -0,0 +1,92 @@
+{
+    // 使用 IntelliSense 了解相关属性。 
+    // 悬停以查看现有属性的描述。
+    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "run_mfd",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/run_mfd.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/config_mfd.yaml" 
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        },
+        {
+            "name": "run_formula_recognition",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/formula_recognition.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/formula_recognition.yaml"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        },
+        {
+            "name": "run_ocr",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/ocr.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/ocr.yaml"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        },
+        {
+            "name": "run_formula_detection",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/formula_detection.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/formula_detection.yaml"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        },
+        {
+            "name": "run_layout_detection",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/layout_detection.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/layout_detection.yaml"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        },
+        {
+            "name": "run_layout_detection_layoutlmv3",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/layout_detection.py",
+            "console": "integratedTerminal",
+            "args": [
+                "--config",
+                "configs/layout_detection_layoutlmv3.yaml"
+            ],
+            "env": {
+                "PYTHONPATH": "/Users/bin/anaconda3/envs/mfd_test"
+            }
+        }
+    ]
+}

PDF-Extract-Kit/LICENSE.md

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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+
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+<https://www.gnu.org/licenses/>.

PDF-Extract-Kit/README.md

@@ -0,0 +1,221 @@
+
+<p align="center">
+  <img src="assets/readme/pdf-extract-kit_logo.png" width="220px" style="vertical-align:middle;">
+</p>
+
+<div align="center">
+
+English | [简体中文](./README_zh-CN.md)
+
+[PDF-Extract-Kit-1.0 Tutorial](https://pdf-extract-kit.readthedocs.io/en/latest/get_started/pretrained_model.html)
+
+[[Models (🤗Hugging Face)]](https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0) | [[Models(<img src="./assets/readme/modelscope_logo.png" width="20px">ModelScope)]](https://www.modelscope.cn/models/OpenDataLab/PDF-Extract-Kit-1.0) 
+ 
+🔥🔥🔥 [MinerU: Efficient Document Content Extraction Tool Based on PDF-Extract-Kit](https://github.com/opendatalab/MinerU)
+
+</div>
+
+<p align="center">
+    👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=MinerU" target="_blank">WeChat</a>
+</p>
+
+
+## Overview
+
+`PDF-Extract-Kit` is a powerful open-source toolkit designed to efficiently extract high-quality content from complex and diverse PDF documents. Here are its main features and advantages:
+
+- **Integration of Leading Document Parsing Models**: Incorporates state-of-the-art models for layout detection, formula detection, formula recognition, OCR, and other core document parsing tasks.
+- **High-Quality Parsing Across Diverse Documents**: Fine-tuned with diverse document annotation data to deliver high-quality results across various complex document types.
+- **Modular Design**: The flexible modular design allows users to easily combine and construct various applications by modifying configuration files and minimal code, making application building as straightforward as stacking blocks.
+- **Comprehensive Evaluation Benchmarks**: Provides diverse and comprehensive PDF evaluation benchmarks, enabling users to choose the most suitable model based on evaluation results.
+
+**Experience PDF-Extract-Kit now and unlock the limitless potential of PDF documents!**
+
+> **Note:** PDF-Extract-Kit is designed for high-quality document processing and functions as a model toolbox.    
+> If you are interested in extracting high-quality document content (e.g., converting PDFs to Markdown), please use [MinerU](https://github.com/opendatalab/MinerU), which combines the high-quality predictions from PDF-Extract-Kit with specialized engineering optimizations for more convenient and efficient content extraction.    
+> If you're a developer looking to create engaging applications such as document translation, document Q&A, or document assistants, you'll find it very convenient to build your own projects using PDF-Extract-Kit. In particular, we will periodically update the PDF-Extract-Kit/project directory with interesting applications, so stay tuned!
+
+**We welcome researchers and engineers from the community to contribute outstanding models and innovative applications by submitting PRs to become contributors to the PDF-Extract-Kit project.**
+
+## Model Overview
+
+| **Task Type**     | **Description**                                                                 | **Models**                    |
+|-------------------|---------------------------------------------------------------------------------|-------------------------------|
+| **Layout Detection** | Locate different elements in a document: including images, tables, text, titles, formulas | `DocLayout-YOLO_ft`, `YOLO-v10_ft`, `LayoutLMv3_ft` | 
+| **Formula Detection** | Locate formulas in documents: including inline and block formulas            | `YOLOv8_ft`                   |  
+| **Formula Recognition** | Recognize formula images into LaTeX source code                             | `UniMERNet`                   |  
+| **OCR**           | Extract text content from images (including location and recognition)            | `PaddleOCR`                   | 
+| **Table Recognition** | Recognize table images into corresponding source code (LaTeX/HTML/Markdown)   | `PaddleOCR+TableMaster`, `StructEqTable` |  
+| **Reading Order** | Sort and concatenate discrete text paragraphs                                    | Coming Soon!                  | 
+
+## News and Updates
+- `2024.10.22` 🎉🎉🎉 We are excited to announce that table recognition model [StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B), which supports output LaTeX, HTML and MarkdDown formats has been officially integrated into `PDF-Extract-Kit 1.0`. Please refer to the [table recognition algorithm documentation](https://pdf-extract-kit.readthedocs.io/en/latest/algorithm/table_recognition.html) for usage instructions!
+- `2024.10.17` 🎉🎉🎉 We are excited to announce that the more accurate and faster layout detection model, [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO), has been officially integrated into `PDF-Extract-Kit 1.0`. Please refer to the [layout detection algorithm documentation](https://pdf-extract-kit.readthedocs.io/en/latest/algorithm/layout_detection.html) for usage instructions!
+- `2024.10.10` 🎉🎉🎉 The official release of `PDF-Extract-Kit 1.0`, rebuilt with modularity for more convenient and flexible model usage! Please switch to the [release/0.1.1](https://github.com/opendatalab/PDF-Extract-Kit/tree/release/0.1.1) branch for the old version.
+- `2024.08.01` 🎉🎉🎉 Added the [StructEqTable](demo/TabRec/StructEqTable/README_TABLE.md) module for table content extraction. Welcome to use it!
+- `2024.07.01` 🎉🎉🎉 We released `PDF-Extract-Kit`, a comprehensive toolkit for high-quality PDF content extraction, including `Layout Detection`, `Formula Detection`, `Formula Recognition`, and `OCR`.
+
+## Performance Demonstration
+
+Many current open-source SOTA models are trained and evaluated on academic datasets, achieving high-quality results only on single document types. To enable models to achieve stable and robust high-quality results on diverse documents, we constructed diverse fine-tuning datasets and fine-tuned some SOTA models to obtain practical parsing models. Below are some visual results of the models.
+
+### Layout Detection
+
+We trained robust `Layout Detection` models using diverse PDF document annotations. Our fine-tuned models achieve accurate extraction results on diverse PDF documents such as papers, textbooks, research reports, and financial reports, and demonstrate high robustness to challenges like blurring and watermarks. The visualization example below shows the inference results of the fine-tuned LayoutLMv3 model.
+ 
+![](assets/readme/layout_example.png)
+
+### Formula Detection
+
+Similarly, we collected and annotated documents containing formulas in both English and Chinese, and fine-tuned advanced formula detection models. The visualization result below shows the inference results of the fine-tuned YOLO formula detection model:
+
+![](assets/readme/mfd_example.png)
+
+### Formula Recognition
+
+[UniMERNet](https://github.com/opendatalab/UniMERNet) is an algorithm designed for diverse formula recognition in real-world scenarios. By constructing large-scale training data and carefully designed results, it achieves excellent recognition performance for complex long formulas, handwritten formulas, and noisy screenshot formulas.
+
+### Table Recognition
+
+[StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) is a high efficiency toolkit that can converts table images into LaTeX/HTML/MarkDown. The latest version, powered by the InternVL2-1B foundation model,  improves Chinese recognition accuracy and expands multi-format output options.
+
+#### For more visual and inference results of the models, please refer to the [PDF-Extract-Kit tutorial documentation](xxx).
+
+## Evaluation Metrics
+
+Coming Soon!
+
+## Usage Guide
+
+### Environment Setup
+
+```bash
+conda create -n pdf-extract-kit-1.0 python=3.10
+conda activate pdf-extract-kit-1.0
+pip install -r requirements.txt
+```
+> **Note:** If your device does not support GPU, please install the CPU version dependencies using `requirements-cpu.txt` instead of `requirements.txt`.
+
+> **Note：** Current Doclayout-YOLO only supports installation from pypi，if error raises during DocLayout-YOLO installation，please install through `pip3 install doclayout-yolo==0.0.2 --extra-index-url=https://pypi.org/simple` .
+
+### Model Download
+
+Please refer to the [Model Weights Download Tutorial](https://pdf-extract-kit.readthedocs.io/en/latest/get_started/pretrained_model.html) to download the required model weights. Note: You can choose to download all the weights or select specific ones. For detailed instructions, please refer to the tutorial.
+
+### Running Demos
+
+#### Layout Detection Model
+
+```bash 
+python scripts/layout_detection.py --config=configs/layout_detection.yaml
+```
+Layout detection models support **DocLayout-YOLO** (default model), YOLO-v10, and LayoutLMv3. For YOLO-v10 and LayoutLMv3, please refer to [Layout Detection Algorithm](https://pdf-extract-kit.readthedocs.io/en/latest/algorithm/layout_detection.html). You can view the layout detection results in the `outputs/layout_detection` folder.
+
+#### Formula Detection Model
+
+```bash 
+python scripts/formula_detection.py --config=configs/formula_detection.yaml
+```
+You can view the formula detection results in the `outputs/formula_detection` folder.
+
+#### OCR Model
+
+```bash 
+python scripts/ocr.py --config=configs/ocr.yaml
+```
+You can view the OCR results in the `outputs/ocr` folder.
+
+#### Formula Recognition Model
+
+```bash 
+python scripts/formula_recognition.py --config=configs/formula_recognition.yaml
+```
+You can view the formula recognition results in the `outputs/formula_recognition` folder.
+
+#### Table Recognition Model
+
+```bash 
+python scripts/table_parsing.py --config configs/table_parsing.yaml
+```
+You can view the table recognition results in the `outputs/table_parsing` folder.
+
+> **Note:** For more details on using the model, please refer to the[PDF-Extract-Kit-1.0 Tutorial](https://pdf-extract-kit.readthedocs.io/en/latest/get_started/pretrained_model.html).
+
+> This project focuses on using models for `high-quality` content extraction from `diverse` documents and does not involve reconstructing extracted content into new documents, such as PDF to Markdown. For such needs, please refer to our other GitHub project: [MinerU](https://github.com/opendatalab/MinerU).
+
+## To-Do List
+
+- [x] **Table Parsing**: Develop functionality to convert table images into corresponding LaTeX/Markdown format source code.
+- [ ] **Chemical Equation Detection**: Implement automatic detection of chemical equations.
+- [ ] **Chemical Equation/Diagram Recognition**: Develop models to recognize and parse chemical equations and diagrams.
+- [ ] **Reading Order Sorting Model**: Build a model to determine the correct reading order of text in documents.
+
+**PDF-Extract-Kit** aims to provide high-quality PDF content extraction capabilities. We encourage the community to propose specific and valuable needs and welcome everyone to participate in continuously improving the PDF-Extract-Kit tool to advance research and industry development.
+
+## License
+
+This project is open-sourced under the [AGPL-3.0](LICENSE) license.
+
+Since this project uses YOLO code and PyMuPDF for file processing, these components require compliance with the AGPL-3.0 license. Therefore, to ensure adherence to the licensing requirements of these dependencies, this repository as a whole adopts the AGPL-3.0 license.
+
+## Acknowledgement
+
+   - [LayoutLMv3](https://github.com/microsoft/unilm/tree/master/layoutlmv3): Layout detection model
+   - [UniMERNet](https://github.com/opendatalab/UniMERNet): Formula recognition model
+   - [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy): Table recognition model
+   - [YOLO](https://github.com/ultralytics/ultralytics): Formula detection model
+   - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR): OCR model
+   - [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO): Layout detection model
+
+## Citation
+If you find our models / code / papers useful in your research, please consider giving ⭐ and citations 📝, thx :)  
+```bibtex
+@article{wang2024mineru,
+  title={MinerU: An Open-Source Solution for Precise Document Content Extraction},
+  author={Wang, Bin and Xu, Chao and Zhao, Xiaomeng and Ouyang, Linke and Wu, Fan and Zhao, Zhiyuan and Xu, Rui and Liu, Kaiwen and Qu, Yuan and Shang, Fukai and others},
+  journal={arXiv preprint arXiv:2409.18839},
+  year={2024}
+}
+
+@misc{zhao2024doclayoutyoloenhancingdocumentlayout,
+      title={DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception}, 
+      author={Zhiyuan Zhao and Hengrui Kang and Bin Wang and Conghui He},
+      year={2024},
+      eprint={2410.12628},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2410.12628}, 
+}
+
+@misc{wang2024unimernet,
+      title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, 
+      author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He},
+      year={2024},
+      eprint={2404.15254},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@article{he2024opendatalab,
+  title={Opendatalab: Empowering general artificial intelligence with open datasets},
+  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
+  journal={arXiv preprint arXiv:2407.13773},
+  year={2024}
+}
+```
+
+## Star History
+
+<a>
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date" />
+ </picture>
+</a>
+
+## Related Links
+- [UniMERNet (Real-World Formula Recognition Algorithm)](https://github.com/opendatalab/UniMERNet)
+- [LabelU (Lightweight Multimodal Annotation Tool)](https://github.com/opendatalab/labelU)
+- [LabelLLM (Open Source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
+- [MinerU (One-Stop High-Quality Data Extraction Tool)](https://github.com/opendatalab/MinerU)

PDF-Extract-Kit/README_zh-CN.md

@@ -0,0 +1,239 @@
+
+<p align="center">
+  <img src="assets/readme/pdf-extract-kit_logo.png" width="220px" style="vertical-align:middle;">
+</p>
+
+<div align="center">
+
+[English](./README.md) | 简体中文
+
+[PDF-Extract-Kit-1.0中文教程](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/get_started/pretrained_model.html)
+
+[[Models (🤗Hugging Face)]](https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0) | [[Models(<img src="./assets/readme/modelscope_logo.png" width="20px">ModelScope)]](https://www.modelscope.cn/models/OpenDataLab/PDF-Extract-Kit-1.0) 
+ 
+🔥🔥🔥 [MinerU：基于PDF-Extract-Kit的高效文档内容提取工具](https://github.com/opendatalab/MinerU)
+</div>
+
+<p align="center">
+    👋 join us on <a href="https://discord.gg/JYsXDXXN" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=MinerU" target="_blank">WeChat</a>
+</p>
+
+
+## 整体介绍
+
+`PDF-Extract-Kit` 是一款功能强大的开源工具箱，旨在从复杂多样的 PDF 文档中高效提取高质量内容。以下是其主要功能和优势：
+
+- **集成文档解析主流模型**：汇聚布局检测、公式检测、公式识别、OCR等文档解析核心任务的众多SOTA模型；
+- **多样性文档下高质量解析结果**：结合多样性文档标注数据在进行模型微调，在复杂多样的文档下提供高质量解析结果；
+- **模块化设计**：模块化设计使用户可以通过修改配置文件及少量代码即可自由组合构建各种应用，让应用构建像搭积木一样简便；  
+- **全面评测基准**：提供多样性全面的PDF评测基准，用户可根据评测结果选择最适合自己的模型。  
+
+**立即体验 PDF-Extract-Kit，解锁 PDF 文档的无限潜力！** 
+
+> **注意：** PDF-Extract-Kit 专注于高质量文档处理，适合作为模型工具箱使用。
+> 如果你想提取高质量文档内容(PDF转Markdown)，请直接使用[MinerU](https://github.com/opendatalab/MinerU)，MinerU结合PDF-Extract-Kit的高质量预测结果，进行了专门的工程优化，使得PDF文档内容提取更加便捷高效；  
+> 如果你是一位开发者，希望搭建更多有意思的应用（如文档翻译，文档问答，文档助手等），基于PDF-Extract-Kit自行进行DIY将会十分便捷。特别地，我们会在`PDF-Extract-Kit/project`下面不定期更新一些有趣的应用，敬请期待！  
+
+**我们欢迎社区研究员和工程师贡献优秀模型和创新应用，通过提交 PR 成为 PDF-Extract-Kit 的贡献者。**
+
+
+## 模型概览
+
+| **任务类型** | **任务描述**                                                                    | **模型**                     |
+|--------------|---------------------------------------------------------------------------------|------------------------------|
+| **布局检测** | 定位文档中不同元素位置：包含图像、表格、文本、标题、公式等 | `DocLayout-YOLO_ft`, `YOLO-v10_ft`, `LayoutLMv3_ft` |
+| **公式检测** | 定位文档中公式位置：包含行内公式和行间公式                                      | `YOLOv8_ft`                       |
+| **公式识别** | 识别公式图像为latex源码                                                         | `UniMERNet`                  |
+|    **OCR**   | 提取图像中的文本内容（包括定位和识别）                                          | `PaddleOCR`                  |
+| **表格识别** | 识别表格图像为对应源码（Latex/HTML/Markdown）                                   | `PaddleOCR+TableMaster`,`StructEqTable`  |
+| **阅读顺序** | 将离散的文本段落进行排序拼接                                                    |  Coming Soon !                            |
+
+
+
+## 新闻和更新
+- `2024.10.22` 🎉🎉🎉 支持LaTex和HTML等多种输出格式的表格模型[StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B)正式接入`PDF-Extract-Kit 1.0`，请参考[表格识别算法文档](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/algorithm/table_recognition.html)进行使用！
+- `2024.10.17` 🎉🎉🎉 检测结果更准确，速度更快的布局检测模型`DocLayout-YOLO`正式接入`PDF-Extract-Kit 1.0`，请参考[布局检测算法文档](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/algorithm/layout_detection.html)进行使用！
+- `2024.10.10` 🎉🎉🎉 基于模块化重构的`PDF-Extract-Kit 1.0`正式版本正式发布，模型使用更加便捷灵活！老版本请切换至[release/0.1.1](https://github.com/opendatalab/PDF-Extract-Kit/tree/release/0.1.1)分支进行使用。
+- `2024.08.01` 🎉🎉🎉 新增了[StructEqTable](demo/TabRec/StructEqTable/README_TABLE.md)表格识别模块用于表格内容提取，欢迎使用！
+- `2024.07.01` 🎉🎉🎉 我们发布了`PDF-Extract-Kit`，一个用于高质量PDF内容提取的综合工具包，包括`布局检测`、`公式检测`、`公式识别`和`OCR`。
+
+
+
+## 效果展示
+
+当前的一些开源SOTA模型多基于学术数据集进行训练评测，仅能在单一的文档类型上获取高质量结果。为了使得模型能够在多样性文档上也能获得稳定鲁棒的高质量结果，我们构建多样性的微调数据集，并在一些SOTA模型上微调已得到可实用解析模型。下边是一些模型的可视化结果。
+
+### 布局检测
+
+结合多样性PDF文档标注，我们训练了鲁棒的`布局检测`模型。在论文、教材、研报、财报等多样性的PDF文档上，我们微调后的模型都能得到准确的提取结果，对于扫描模糊、水印等情况也有较高鲁棒性。下面可视化示例是经过微调后的LayoutLMv3模型的推理结果。
+
+![](assets/readme/layout_example.png)
+
+
+### 公式检测
+
+同样的，我们收集了包含公式的中英文文档进行标注，基于先进的公式检测模型进行微调，下面可视化结果是微调后的YOLO公式检测模型的推理结果：
+
+![](assets/readme/mfd_example.png)
+
+
+### 公式识别
+
+[UniMERNet](https://github.com/opendatalab/UniMERNet)是针对真实场景下多样性公式识别的算法，通过构建大规模训练数据及精心设计的结果，使得其可以对复杂长公式、手写公式、含噪声的截图公式均有不错的识别效果。
+
+### 表格识别
+
+[StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)是一个高效表格内容提取工具，能够将表格图像转换为LaTeX/HTML/Markdown格式，最新版本使用InternVL2-1B基础模型，提高了中文识别准确度并增加了多格式输出能力。
+
+#### 更多模型的可视化结果及推理结果可以参考[PDF-Extract-Kit教程文档](xxx)
+
+
+## 评测指标
+
+Coming Soon! 
+
+## 使用教程
+
+### 环境安装
+
+```bash
+conda create -n pdf-extract-kit-1.0 python=3.10
+conda activate pdf-extract-kit-1.0
+pip install -r requirements.txt
+```
+> **注意：** 如果你的设备不支持 GPU，请使用 `requirements-cpu.txt` 安装 CPU 版本的依赖。
+
+> **注意：** 目前doclayout-yolo仅支持从pypi源安装，如果出现doclayout-yolo无法安装，请通过 `pip3 install doclayout-yolo==0.0.2 --extra-index-url=https://pypi.org/simple` 安装。
+
+### 模型下载
+
+参考[模型权重下载教程](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/get_started/pretrained_model.html)下载所需模型权重。注：可以选择全部下载，也可以选择部分下载，具体操作参考教程。
+
+
+### Demo运行
+
+#### 布局检测模型
+
+```bash 
+python scripts/layout_detection.py --config=configs/layout_detection.yaml
+```
+布局检测模型支持**DocLayout-YOLO**（默认模型），YOLO-v10，以及LayoutLMv3。对于YOLO-v10和LayoutLMv3的布局检测，请参考[Layout Detection Algorithm](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/algorithm/layout_detection.html)。你可以在 `outputs/layout_detection` 文件夹下查看布局检测结果。
+
+#### 公式检测模型
+
+```bash 
+python scripts/formula_detection.py --config=configs/formula_detection.yaml
+```
+你可以在 `outputs/formula_detection` 文件夹下查看公式检测结果。
+
+
+#### 文本识别（OCR）模型
+
+```bash 
+python scripts/ocr.py --config=configs/ocr.yaml
+```
+你可以在 `outputs/ocr` 文件夹下查看OCR结果。
+
+
+#### 公式识别模型
+
+```bash 
+python scripts/formula_recognition.py --config=configs/formula_recognition.yaml
+```
+你可以在 `outputs/formula_recognition` 文件夹下查看公式识别结果。
+
+
+#### 表格识别模型
+
+```bash 
+python scripts/table_parsing.py --config configs/table_parsing.yaml
+```
+你可以在 `outputs/table_parsing` 文件夹下查看表格内容识别结果。
+
+
+> **注意：** 更多模型使用细节请查看[PDF-Extract-Kit-1.0 中文教程](https://pdf-extract-kit.readthedocs.io/zh-cn/latest/get_started/pretrained_model.html).
+
+> 本项目专注使用模型对`多样性`文档进行`高质量`内容提取，不涉及提取后内容拼接成新文档，如PDF转Markdown。如果有此类需求，请参考我们另一个Github项目: [MinerU](https://github.com/opendatalab/MinerU)
+
+
+## 待办事项
+
+- [x] **表格解析**：开发能够将表格图像转换成对应的LaTeX/Markdown格式源码的功能。  
+- [ ] **化学方程式检测**：实现对化学方程式的自动检测。  
+- [ ] **化学方程式/图解识别**：开发识别并解析化学方程式的模型。  
+- [ ] **阅读顺序排序模型**：构建模型以确定文档中文本的正确阅读顺序。  
+
+**PDF-Extract-Kit** 旨在提供高质量PDF文件的提取能力。我们鼓励社区提出具体且有价值的需求，并欢迎大家共同参与，以不断改进PDF-Extract-Kit工具，推动科研及产业发展。
+
+
+## 协议
+
+本项目采用 [AGPL-3.0](LICENSE) 协议开源。
+
+由于本项目中使用了 YOLO 代码和 PyMuPDF 进行文件处理，这些组件都需要遵循 AGPL-3.0 协议。因此，为了确保遵守这些依赖项的许可证要求，本仓库整体采用 AGPL-3.0 协议。
+
+
+## 致谢
+
+   - [LayoutLMv3](https://github.com/microsoft/unilm/tree/master/layoutlmv3): 布局检测模型
+   - [UniMERNet](https://github.com/opendatalab/UniMERNet): 公式识别模型
+   - [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy): 表格识别模型
+   - [YOLO](https://github.com/ultralytics/ultralytics): 公式检测模型
+   - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR): OCR模型
+   - [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO): 布局检测模型
+
+
+## Citation
+
+如果你觉得我们模型/代码/技术报告对你有帮助，请给我们⭐和引用📝,谢谢 :)  
+```bibtex
+@article{wang2024mineru,
+  title={MinerU: An Open-Source Solution for Precise Document Content Extraction},
+  author={Wang, Bin and Xu, Chao and Zhao, Xiaomeng and Ouyang, Linke and Wu, Fan and Zhao, Zhiyuan and Xu, Rui and Liu, Kaiwen and Qu, Yuan and Shang, Fukai and others},
+  journal={arXiv preprint arXiv:2409.18839},
+  year={2024}
+}
+
+@misc{wang2024unimernet,
+      title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, 
+      author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He},
+      year={2024},
+      eprint={2404.15254},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{zhao2024doclayoutyoloenhancingdocumentlayout,
+      title={DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception}, 
+      author={Zhiyuan Zhao and Hengrui Kang and Bin Wang and Conghui He},
+      year={2024},
+      eprint={2410.12628},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2410.12628}, 
+}
+
+@article{he2024opendatalab,
+  title={Opendatalab: Empowering general artificial intelligence with open datasets},
+  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
+  journal={arXiv preprint arXiv:2407.13773},
+  year={2024}
+}
+```
+
+
+## Star历史
+
+<a>
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/PDF-Extract-Kit&type=Date" />
+ </picture>
+</a>
+
+## 友情链接
+- [UniMERNet（真实场景公式识别算法）](https://github.com/opendatalab/UniMERNet)
+- [LabelU（轻量级多模态标注工具）](https://github.com/opendatalab/labelU)
+- [LabelLLM（开源LLM对话标注平台）](https://github.com/opendatalab/LabelLLM)
+- [MinerU（一站式高质量数据提取工具）](https://github.com/opendatalab/MinerU)

PDF-Extract-Kit/assets/demo/PDFs/DDPM.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aee5e07a802e8dfd2a386374c94fd61d1d056cb7e1e0fec4f28e8120ff5d8505
+size 10267274

PDF-Extract-Kit/assets/demo/PDFs/UniMERNet.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66a7fb0a7ea057c3222893b838cdae1ef20d79acecb265bbc35333509a99e2dd
+size 6888984

PDF-Extract-Kit/configs/config.yaml

@@ -0,0 +1,17 @@
+inputs: assets/demo/formula_detection_pdfs
+outputs: outputs/formula_detection_pdfs
+tasks:
+  formula_detection:
+    model: formula_detection_yolo
+    model_config:
+      img_size: 1280
+      conf_thres: 0.25
+      iou_thres: 0.45
+      model_path: models/MFD/weights.pt
+      visualize: True
+  formula_recognition:
+    model: formula_recognition_unimernet
+    model_config:
+      cfg_path: pdf_extract_kit/configs/unimernet.yaml
+      model_path: models/MFR/UniMERNet
+      visualize: True

PDF-Extract-Kit/configs/formula_detection.yaml

@@ -0,0 +1,12 @@
+inputs: assets/demo/formula_detection
+outputs: outputs/formula_detection
+tasks:
+  formula_detection:
+    model: formula_detection_yolo
+    model_config:
+      img_size: 1280
+      conf_thres: 0.25
+      iou_thres: 0.45
+      batch_size: 1
+      model_path: models/MFD/YOLO/yolo_v8_ft.pt
+      visualize: True

PDF-Extract-Kit/configs/formula_recognition.yaml

@@ -0,0 +1,9 @@
+inputs: assets/demo/formula_recognition
+outputs: outputs/formula_recognition
+tasks:
+  formula_recognition:
+    model: formula_recognition_unimernet
+    model_config:
+      cfg_path: pdf_extract_kit/configs/unimernet.yaml
+      model_path: models/MFR/unimernet_tiny
+      visualize: False

PDF-Extract-Kit/configs/layout_detection.yaml

@@ -0,0 +1,11 @@
+inputs: assets/demo/layout_detection
+outputs: outputs/layout_detection
+tasks:
+  layout_detection:
+    model: layout_detection_yolo
+    model_config:
+      img_size: 1024
+      conf_thres: 0.25
+      iou_thres: 0.45
+      model_path: models/Layout/YOLO/doclayout_yolo_ft.pt
+      visualize: True

PDF-Extract-Kit/configs/layout_detection_layoutlmv3.yaml

@@ -0,0 +1,7 @@
+inputs: assets/demo/layout_detection
+outputs: outputs/layout_detection
+tasks:
+  layout_detection:
+    model: layout_detection_layoutlmv3
+    model_config:
+      model_path: models/Layout/LayoutLMv3/model_final.pth

PDF-Extract-Kit/configs/layout_detection_yolo.yaml

@@ -0,0 +1,12 @@
+inputs: assets/demo/layout_detection
+outputs: outputs/layout_detection
+tasks:
+  layout_detection:
+    model: layout_detection_yolo
+    model_config:
+      img_size: 1024
+      conf_thres: 0.25
+      iou_thres: 0.45
+      model_path: models/Layout/YOLO/doclayout_yolo_ft.pt
+      visualize: True
+      device: 0

PDF-Extract-Kit/configs/ocr.yaml

@@ -0,0 +1,12 @@
+inputs: assets/demo/ocr
+outputs: outputs/ocr
+visualize: True
+tasks:
+  ocr:
+    model: ocr_ppocr
+    model_config:
+      lang: ch
+      show_log: True
+      det_model_dir: models/OCR/PaddleOCR/det/ch_PP-OCRv4_det
+      rec_model_dir: models/OCR/PaddleOCR/rec/ch_PP-OCRv4_rec
+      det_db_box_thresh: 0.3

PDF-Extract-Kit/configs/table_parsing.yaml

@@ -0,0 +1,12 @@
+inputs: assets/demo/table_parsing
+outputs: outputs/table_parsing
+tasks:
+  table_parsing:
+    model: table_parsing_struct_eqtable
+    model_config:
+      model_path: models/TabRec/StructEqTable
+      max_new_tokens: 1024
+      max_time: 30
+      output_format: latex
+      lmdeploy: False
+      flash_atten: True

PDF-Extract-Kit/docs/en/.readthedocs.yaml

@@ -0,0 +1,16 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: requirements/docs.txt
+
+sphinx:
+  configuration: docs/en/conf.py

PDF-Extract-Kit/docs/en/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

PDF-Extract-Kit/docs/en/algorithm/formula_detection.rst

@@ -0,0 +1,91 @@
+..  _algorithm_formula_detection:
+
+====================
+Formula Detection Algorithm
+====================
+
+Introduction
+====================
+
+Formula detection involves identifying the positions of all formulas (including inline and block formulas) in a given input image.
+
+.. note::
+
+   Formula detection is technically a subtask of layout detection. However, due to its complexity, we recommend using a dedicated formula detection model to decouple it. This approach typically makes data annotation easier and improves detection performance.
+
+Model Usage
+====================
+
+With the environment properly set up, simply run the layout detection algorithm script by executing ``scripts/formula_detection.py``.
+
+.. code:: shell
+
+   $ python scripts/formula_detection.py --config configs/formula_detection.yaml
+
+Model Configuration
+--------------------
+
+.. code:: yaml
+
+   inputs: assets/demo/formula_detection
+   outputs: outputs/formula_detection
+   tasks:
+      formula_detection:
+         model: formula_detection_yolo
+         model_config:
+            img_size: 1280
+            conf_thres: 0.25
+            iou_thres: 0.45
+            batch_size: 1
+            model_path: models/MFD/yolov8/weights.pt
+            visualize: True
+
+- inputs/outputs: Define the input file path and the visualization output directory, respectively.
+- tasks: Define the task type, currently only a formula detection task is included.
+- model: Define the specific model type: currently, only the YOLO formula detection model is available.
+- model_config: Define the model configuration.
+- img_size: Define the image's longer side size; the shorter side will be scaled proportionally.
+- conf_thres: Define the confidence threshold; only targets above this threshold will be detected.
+- iou_thres: Define the IoU threshold to remove targets with an overlap greater than this value.
+- batch_size: Define the batch size; the number of images inferred simultaneously. Generally, the larger the batch size, the faster the inference speed. A better GPU allows for a larger batch size.
+- model_path: Path to the model weights.
+- visualize: Whether to visualize the model results. Visualized results will be saved in the outputs directory.
+
+Diverse Input Support
+--------------------
+
+The formula detection script in PDF-Extract-Kit supports various input formats such as ``a single image``, ``a directory of image files``, ``a single PDF file``, and ``a directory of PDF files``.
+
+.. note:: 
+
+   Modify the ``inputs`` path in ``configs/formula_detection.yaml`` according to your actual data format:
+   - Single image: path/to/image  
+   - Image directory: path/to/images  
+   - Single PDF file: path/to/pdf  
+   - PDF directory: path/to/pdfs  
+
+.. note::
+
+   When using a PDF as input, you need to change ``predict_images`` to ``predict_pdfs`` in ``formula_detection.py``.
+
+   .. code:: python
+
+      # for image detection
+      detection_results = model_formula_detection.predict_images(input_data, result_path)
+   
+   Change to:
+
+   .. code:: python
+
+      # for pdf detection
+      detection_results = model_formula_detection.predict_pdfs(input_data, result_path)
+
+
+Viewing Visualization Results
+--------------------
+
+When the ``visualize`` option in the config file is set to ``True``, visualization results will be saved in the ``outputs/formula_detection`` directory.
+
+.. note::
+
+   Visualization facilitates the analysis of model results. However, for large-scale tasks, it is recommended to disable visualization (set ``visualize`` to ``False`` ) to reduce memory and disk usage.

PDF-Extract-Kit/docs/en/algorithm/formula_recognition.rst

@@ -0,0 +1,52 @@
+..  _algorithm_formula_recognition:
+
+============
+Formula Recognition Algorithm
+============
+
+Introduction
+=================
+
+Formula detection involves recognizing the content of a given input formula image and converting it to ``LaTeX`` format.
+
+Model Usage
+=================
+
+With the environment properly configured, you can run the layout detection algorithm script by executing ``scripts/formula_recognition.py``.
+
+.. code:: shell
+
+   $ python scripts/formula_recognition.py --config configs/formula_recognition.yaml
+
+Model Configuration
+-----------------
+
+.. code:: yaml
+
+   inputs: assets/demo/formula_recognition
+   outputs: outputs/formula_recognition
+   tasks:
+      formula_recognition:
+         model: formula_recognition_unimernet
+         model_config:
+            cfg_path: pdf_extract_kit/configs/unimernet.yaml
+            model_path: models/MFR/unimernet_tiny
+            visualize: False
+
+- inputs/outputs: Define the input file path and the directory for LaTeX prediction results, respectively.
+- tasks: Define the task type, currently only containing a formula recognition task.
+- model: Define the specific model type: Currently, only the `UniMERNet <https://github.com/opendatalab/UniMERNet>`_ formula recognition model is provided.
+- model_config: Define the model configuration.
+- cfg_path: Path to the UniMERNet configuration file.
+- model_path: Path to the model weights.
+- visualize: Whether to visualize the model results. Visualized results will be saved in the outputs directory.
+
+Support for Diverse Inputs
+-----------------
+
+The formula detection script in PDF-Extract-Kit supports ``single formula images`` and ``document images with corresponding formula regions``.
+
+Viewing Visualization Results
+-----------------
+
+When the visualize setting in the config file is set to True, ``LaTeX`` prediction results will be saved in the outputs directory.

PDF-Extract-Kit/docs/en/algorithm/layout_detection.rst

@@ -0,0 +1,190 @@
+.. _algorithm_layout_detection:
+
+=================
+Layout Detection Algorithm
+=================
+
+Introduction
+=================
+
+Layout detection is a fundamental task in document content extraction, aiming to locate different types of regions on a page, such as images, tables, text, and headings, to facilitate high-quality content extraction. For text and heading regions, OCR models can be used for text recognition, while table regions can be converted using table recognition models.
+
+Model Usage
+=================
+
+Layout detection supports following models：
+
+.. raw:: html
+
+    <style type="text/css">
+    .tg  {border-collapse:collapse;border-color:#9ABAD9;border-spacing:0;}
+    .tg td{background-color:#EBF5FF;border-color:#9ABAD9;border-style:solid;border-width:1px;color:#444;
+      font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;word-break:normal;}
+    .tg th{background-color:#409cff;border-color:#9ABAD9;border-style:solid;border-width:1px;color:#fff;
+      font-family:Arial, sans-serif;font-size:14px;font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
+    .tg .tg-f8tz{background-color:#409cff;border-color:inherit;text-align:left;vertical-align:top}
+    .tg .tg-0lax{text-align:left;vertical-align:top}
+    .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}
+    </style>
+    <table class="tg"><thead>
+      <tr>
+        <th class="tg-0lax">Model</th>
+        <th class="tg-f8tz">Description</th>
+        <th class="tg-f8tz">Characteristics</th>
+        <th class="tg-f8tz">Model weight</th>
+        <th class="tg-f8tz">Config file</th>
+      </tr></thead>
+    <tbody>
+      <tr>
+        <td class="tg-0lax">DocLayout-YOLO</td>
+        <td class="tg-0pky">Improved based on YOLO-v10：<br>1. Generate diverse pre-training data，enhance generalization ability across multiple document types<br>2. Model architecture improvement, improve perception ability on scale-varing instances<br>Details in <a href="https://github.com/opendatalab/DocLayout-YOLO" target="_blank" rel="noopener noreferrer">DocLayout-YOLO</a></td>
+        <td class="tg-0pky">Speed:Fast, Accuracy:High</td>
+        <td class="tg-0pky"><a href="https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0/blob/main/models/Layout/YOLO/doclayout_yolo_ft.pt" target="_blank" rel="noopener noreferrer">doclayout_yolo_ft.pt</a></td>
+        <td class="tg-0pky">layout_detection.yaml</td>
+      </tr>
+      <tr>
+        <td class="tg-0lax">YOLO-v10</td>
+        <td class="tg-0pky">Base YOLO-v10 model</td>
+        <td class="tg-0pky">Speed:Fast, Accuracy:Moderate</td>
+        <td class="tg-0pky"><a href="https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0/blob/main/models/Layout/YOLO/yolov10l_ft.pt" target="_blank" rel="noopener noreferrer">yolov10l_ft.pt</a></td>
+        <td class="tg-0pky">layout_detection_yolo.yaml</td>
+      </tr>
+      <tr>
+        <td class="tg-0lax">LayoutLMv3</td>
+        <td class="tg-0pky">Base LayoutLMv3 model</td>
+        <td class="tg-0pky">Speed:Slow, Accuracy:High</td>
+        <td class="tg-0pky"><a href="https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0/tree/main/models/Layout/LayoutLMv3" target="_blank" rel="noopener noreferrer">layoutlmv3_ft</a></td>
+        <td class="tg-0pky">layout_detection_layoutlmv3.yaml</td>
+      </tr>
+    </tbody></table>
+
+Once enciroment is setup, you can perform layout detection by executing ``scripts/layout_detection.py`` directly.
+
+**Run demo**
+
+.. code:: shell
+
+   $ python scripts/layout_detection.py --config configs/layout_detection.yaml
+
+Model Configuration
+-----------------
+
+**1. DocLayout-YOLO / YOLO-v10**
+
+.. code:: yaml
+
+    inputs: assets/demo/layout_detection
+    outputs: outputs/layout_detection
+    tasks:
+      layout_detection:
+        model: layout_detection_yolo
+        model_config:
+          img_size: 1024
+          conf_thres: 0.25
+          iou_thres: 0.45
+          model_path: path/to/doclayout_yolo_model
+          visualize: True
+
+- inputs/outputs: Define the input file path and the directory for visualization output.
+- tasks: Define the task type, currently only a layout detection task is included.
+- model: Specify the specific model type, e.g., layout_detection_yolo.
+- model_config: Define the model configuration.
+- img_size: Define the image long edge size; the short edge will be scaled proportionally based on the long edge, with the default long edge being 1024.
+- conf_thres: Define the confidence threshold, detecting only targets above this threshold.
+- iou_thres: Define the IoU threshold, removing targets with an overlap greater than this threshold.
+- model_path: Path to the model weights.
+- visualize: Whether to visualize the model results; visualized results will be saved in the outputs directory.
+
+
+**2. layoutlmv3**
+
+.. note::
+   
+   LayoutLMv3 cannot run directly by default. Please follow the steps below to modify the configuration:
+
+   1. **Detectron2 Environment Setup**
+
+   .. code-block:: bash
+
+      # For Linux
+      pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-linux_x86_64.whl
+
+      # For macOS
+      pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-macosx_10_9_universal2.whl
+
+      # For Windows
+      pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-win_amd64.whl
+
+   2. **Enable LayoutLMv3 Registration Code**
+
+   Uncomment the lines at the following links:
+   
+   - `line 2 <https://github.com/opendatalab/PDF-Extract-Kit/blob/main/pdf_extract_kit/tasks/layout_detection/__init__.py#L2>`_
+   - `line 8 <https://github.com/opendatalab/PDF-Extract-Kit/blob/main/pdf_extract_kit/tasks/layout_detection/__init__.py#L8>`_
+
+   .. code-block:: python
+
+      from pdf_extract_kit.tasks.layout_detection.models.yolo import LayoutDetectionYOLO
+      from pdf_extract_kit.tasks.layout_detection.models.layoutlmv3 import LayoutDetectionLayoutlmv3
+      from pdf_extract_kit.registry.registry import MODEL_REGISTRY
+
+      __all__ = [
+         "LayoutDetectionYOLO",
+         "LayoutDetectionLayoutlmv3",
+      ]
+
+
+.. code:: yaml
+
+    inputs: assets/demo/layout_detection
+    outputs: outputs/layout_detection
+    tasks:
+      layout_detection:
+        model: layout_detection_layoutlmv3
+        model_config:
+          model_path: path/to/layoutlmv3_model
+
+- inputs/outputs: Define the input file path and the directory for visualization output.
+- tasks: Define the task type, currently only a layout detection task is included.
+- model: Specify the specific model type, e.g., layout_detection_layoutlmv3.
+- model_config: Define the model configuration.
+- model_path: Path to the model weights.
+
+
+
+Diverse Input Support
+-----------------
+
+The layout detection script in PDF-Extract-Kit supports input formats such as a ``single image``, a ``directory containing only image files``, a ``single PDF file``, and a ``directory containing only PDF files``.
+
+.. note::
+
+   Modify the path to inputs in configs/layout_detection.yaml according to your actual data format:
+   - Single image: path/to/image  
+   - Image directory: path/to/images  
+   - Single PDF file: path/to/pdf  
+   - PDF directory: path/to/pdfs  
+
+.. note::
+   When using PDF as input, you need to change ``predict_images`` to ``predict_pdfs`` in ``layout_detection.py``.
+
+   .. code:: python
+
+      # for image detection
+      detection_results = model_layout_detection.predict_images(input_data, result_path)
+
+   Change to:
+
+   .. code:: python
+
+      # for pdf detection
+      detection_results = model_layout_detection.predict_pdfs(input_data, result_path)
+
+Viewing Visualization Results
+-----------------
+
+When ``visualize`` is set to ``True`` in the config file, the visualization results will be saved in the ``outputs`` directory.
+
+.. note::
+
+   Visualization is helpful for analyzing model results, but for large-scale tasks, it is recommended to turn off visualization (set ``visualize`` to ``False`` ) to reduce memory and disk usage.

PDF-Extract-Kit/docs/en/algorithm/ocr.rst

@@ -0,0 +1,65 @@
+..  _algorithm_ocr:
+==========================
+OCR (Optical Character Recognition) Algorithm
+==========================
+
+Introduction
+====================
+
+OCR(Optical Character Recognition) involves identifying the positions ajnd contents of all text blocks in pictures.
+
+
+Model Usage
+====================
+
+With the environment properly set up, simply run the ocr algorithm script by executing ``scripts/ocr.py`` .
+
+.. code:: shell
+
+   $ python scripts/ocr.py --config configs/ocr.yaml
+
+
+Model Configuration
+--------------------
+
+.. code:: yaml
+
+   inputs: assets/demo/ocr
+   outputs: outputs/ocr
+   visualize: True
+   tasks:
+      ocr:
+         model: ocr_ppocr
+         model_config:
+            lang: ch
+            show_log: True
+            det_model_dir: models/OCR/PaddleOCR/det/ch_PP-OCRv4_det
+            rec_model_dir: models/OCR/PaddleOCR/rec/ch_PP-OCRv4_rec
+            det_db_box_thresh: 0.3
+
+- inputs/outputs: Define the input path and the output path, respectively.
+- visualize: Whether to visualize the model results. Visualized results will be saved in the outputs directory.
+- tasks: Define the task type, currently only a OCR task is included.
+- model: Define the specific model type, currently, only the PaddleOCR model is available.
+- model_config: Define the model configuration.
+- lang: Define the language, default language ch supports both english and chinese.
+- show_log: Whether to print running logs.
+- det_model_dir: Define the path of PaddleOCR' detection model, If the specified path does not exist, the model weight will be automatically downloaded to the path.
+- rec_model_dir: Define the path of PaddleOCR' recognize model, If the specified path does not exist, the model weight will be automatically downloaded to the path.
+- det_db_box_thresh: Confidence filter threshold, bounding boxes whose confidence is lower than the threshold are discarded.
+
+
+Diverse Input Support
+--------------------
+
+The OCR script in PDF-Extract-Kit supports various input formats such as ``a single image/PDF``, ``a directory of image/PDF files``.
+
+
+Viewing Visualization Results
+--------------------
+
+When the ``visualize`` option in the config file is set to ``True``, visualization results will be saved in the ``outputs`` directory.
+
+.. note::
+
+   Visualization facilitates the analysis of model results. However, for large-scale tasks, it is recommended to disable visualization (set ``visualize`` to ``False`` ) to reduce memory and disk usage.

PDF-Extract-Kit/docs/en/algorithm/reading_order.rst

@@ -0,0 +1,6 @@
+..  _algorithm_reading_oder:
+==============
+Reading Order Algorithm
+==============
+
+Comming soon.

PDF-Extract-Kit/docs/en/algorithm/table_recognition.rst

@@ -0,0 +1,66 @@
+..  _algorithm_table_recognition:
+
+========================
+Table Recognition Algorithm
+========================
+
+Introduction
+=================
+
+Table recognition refers to the process of inputting a table image, identifying the table structure and content, and converting it into formats such as ``LaTeX`` or ``HTML``.
+
+Model Usage
+=================
+
+With the environment properly configured, you can run the table recognition algorithm script by directly executing ``scripts/table_parsing.py``.
+
+.. code:: shell
+
+   $ python scripts/table_parsing.py --config configs/table_parsing.yaml
+
+Model Configuration
+-----------------
+
+.. code:: yaml
+
+    inputs: assets/demo/table_parsing
+    outputs: outputs/table_parsing
+    tasks:
+      table_parsing:
+        model: table_parsing_struct_eqtable
+        model_config:
+          model_path: models/TabRec/StructEqTable
+          max_new_tokens: 1024
+          max_time: 30
+          output_format: latex
+          lmdeploy: False
+          flash_attn: True
+
+- inputs/outputs: Define the input file path and table recognition result directory respectively
+- tasks: Define the task type, currently only including one table recognition task
+- model: Define the specific model type: currently using the `StructEqTable <https://github.com/UniModal4Reasoning/StructEqTable-Deploy>`_ table recognition model
+- model_config: Define the model configuration
+- model_path: Path to the model weights
+- max_new_tokens: Maximum number of tokens to generate, default is 1024, maximum supported is 4096
+- max_time: Maximum runtime for the model (in seconds)
+- output_format: Output format, default is set to ``latex``, options include ``html`` and ``markdown``
+- lmdeploy: Whether to use LMDeploy for deployment, currently set to False
+- flash_attn: Whether to use flash attention, only available for Ampere GPUs
+
+Diverse Input Support
+-----------------
+
+The table recognition script in PDF-Extract-Kit supports ``single table images`` and ``multiple table images`` as input.
+
+.. note::
+
+   The StructEqTable model only supports running on GPU devices
+
+.. note::
+    
+    Adjust ``max_new_tokens`` and ``max_time`` according to the table content, defaults are 1024 and 30 respectively.
+
+.. note::
+    
+    lmdeploy is an option for accelerated inference. If set to True, it will use LMDeploy for accelerated inference deployment.
+    To use LMDeploy deployment, you need to install LMDeploy. For installation methods, refer to `LMDeploy <https://github.com/InternLM/lmdeploy>`_.

PDF-Extract-Kit/docs/en/conf copy.py

@@ -0,0 +1,121 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+
+import os
+import subprocess
+import sys
+
+# def install(package):
+#     subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# # 安装 requirements.txt 中的依赖项
+# requirements_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'requirements.txt'))
+# if os.path.exists(requirements_path):
+#     with open(requirements_path) as f:
+#         packages = f.readlines()
+#     for package in packages:
+#         install(package.strip())
+
+from sphinx.ext import autodoc
+
+sys.path.insert(0, os.path.abspath('../..'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'PDF-Extract-Kit'
+copyright = '2024, OpenDataLab'
+author = 'PDF-Extract-Kit Contributors'
+
+# The full version, including alpha/beta/rc tags
+version_file = '../../pdf_extract_kit/version.py'
+with open(version_file) as f:
+    exec(compile(f.read(), version_file, 'exec'))
+__version__ = locals()['__version__']
+# The short X.Y version
+version = __version__
+# The full version, including alpha/beta/rc tags
+release = __version__
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.intersphinx',
+    'sphinx_copybutton',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'myst_parser',
+    'sphinxarg.ext',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# Exclude the prompt "$" when copying code
+copybutton_prompt_text = r'\$ '
+copybutton_prompt_is_regexp = True
+
+language = 'zh_CN'
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_book_theme'
+html_logo = '_static/image/logo.png'
+html_theme_options = {
+    'path_to_docs': 'docs/zh_cn',
+    'repository_url': 'https://github.com/opendatalab/PDF-Extract-Kit',
+    'use_repository_button': True,
+}
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+# html_static_path = ['_static']
+
+# Mock out external dependencies here.
+autodoc_mock_imports = [
+    'cpuinfo',
+    'torch',
+    'transformers',
+    'psutil',
+    'prometheus_client',
+    'sentencepiece',
+    'vllm.cuda_utils',
+    'vllm._C',
+    'numpy',
+    'tqdm',
+]
+
+
+class MockedClassDocumenter(autodoc.ClassDocumenter):
+    """Remove note about base class when a class is derived from object."""
+
+    def add_line(self, line: str, source: str, *lineno: int) -> None:
+        if line == '   Bases: :py:class:`object`':
+            return
+        super().add_line(line, source, *lineno)
+
+
+autodoc.ClassDocumenter = MockedClassDocumenter
+
+navigation_with_keys = False