Merge pull request #966 from opendatalab/dev

Dev to 0.9.3

Merge pull request #966 from opendatalab/dev
Dev to 0.9.3
23d83968 · Xiaomeng Zhao · GitHub · 9581fcda · d44a868a · 23d83968
Unverified Commit 23d83968 authored Nov 15, 2024 by Xiaomeng Zhao Committed by GitHub Nov 15, 2024
20 changed files
--- a/Dockerfile
+++ b/Dockerfile
@@ -42,7 +42,7 @@ RUN /bin/bash -c "wget https://gitee.com/myhloli/MinerU/raw/master/magic-pdf.tem
 # Download models and update the configuration file
 RUN /bin/bash -c "pip3 install modelscope && \
-    wget https://gitee.com/myhloli/MinerU/raw/master/docs/download_models.py && \
+    wget https://gitee.com/myhloli/MinerU/raw/master/scripts/download_models.py && \
    python3 download_models.py && \
    sed -i 's|/tmp/models|/root/.cache/modelscope/hub/opendatalab/PDF-Extract-Kit/models|g' /root/magic-pdf.json && \
    sed -i 's|cpu|cuda|g' /root/magic-pdf.json"

--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/3b3a00a4a0a61577b6c30f989092d20d/mineru_demo.ipynb)
 [![Paper](https://img.shields.io/badge/Paper-arXiv-green)](https://arxiv.org/abs/2409.18839)
 <a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 <!-- language -->
@@ -41,6 +42,7 @@
 </div>
 # Changelog
+- 2024/11/15 0.9.3 released. Integrated [RapidTable](https://github.com/RapidAI/RapidTable) for table recognition, improving single-table parsing speed by more than 10 times, with higher accuracy and lower GPU memory usage.
 - 2024/11/06 0.9.2 released. Integrated the [StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B) model for table recognition functionality.
 - 2024/10/31 0.9.0 released. This is a major new version with extensive code refactoring, addressing numerous issues, improving performance, reducing hardware requirements, and enhancing usability:
  - Refactored the sorting module code to use [layoutreader](https://github.com/ppaanngggg/layoutreader) for reading order sorting, ensuring high accuracy in various layouts.
@@ -75,10 +77,12 @@
            <ul>
            <li><a href="#online-demo">Online Demo</a></li>
            <li><a href="#quick-cpu-demo">Quick CPU Demo</a></li>
+            <li><a href="#using-gpu">Using GPU</a></li>
            </ul>
        </li>
        <li><a href="#usage">Usage</a>
            <ul>
+            <li><a href="#command-line">Command Line</a></li>
            <li><a href="#api">API</a></li>
            <li><a href="#deploy-derived-projects">Deploy Derived Projects</a></li>
            <li><a href="#development-guide">Development Guide</a></li>
@@ -87,6 +91,8 @@
      </ul>
    </li>
    <li><a href="#todo">TODO</a></li>
+    <li><a href="#known-issues">Known Issues</a></li>
+    <li><a href="#faq">FAQ</a></li>
    <li><a href="#all-thanks-to-our-contributors">All Thanks To Our Contributors</a></li>
    <li><a href="#license-information">License Information</a></li>
    <li><a href="#acknowledgments">Acknowledgments</a></li>
@@ -108,12 +114,89 @@ Compared to well-known commercial products, MinerU is still young. If you encoun
 https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
+## Key Features
+- Remove headers, footers, footnotes, page numbers, etc., to ensure semantic coherence.
+- Output text in human-readable order, suitable for single-column, multi-column, and complex layouts.
+- Preserve the structure of the original document, including headings, paragraphs, lists, etc.
+- Extract images, image descriptions, tables, table titles, and footnotes.
+- Automatically recognize and convert formulas in the document to LaTeX format.
+- Automatically recognize and convert tables in the document to LaTeX or HTML format.
+- Automatically detect scanned PDFs and garbled PDFs and enable OCR functionality.
+- OCR supports detection and recognition of 84 languages.
+- Supports multiple output formats, such as multimodal and NLP Markdown, JSON sorted by reading order, and rich intermediate formats.
+- Supports various visualization results, including layout visualization and span visualization, for efficient confirmation of output quality.
+- Supports both CPU and GPU environments.
+- Compatible with Windows, Linux, and Mac platforms.
 ## Quick Start
-There are multiple different ways to experience MinerU:
+If you encounter any installation issues, please first consult the <a href="#faq">FAQ</a>. </br>
+If the parsing results are not as expected, refer to the <a href="#known-issues">Known Issues</a>. </br>
+There are three different ways to experience MinerU:
 - [Online Demo (No Installation Required)](#online-demo)
 - [Quick CPU Demo (Windows, Linux, Mac)](#quick-cpu-demo)
+- [Linux/Windows + CUDA](#Using-GPU)
+> [!WARNING]
+> **Pre-installation Notice—Hardware and Software Environment Support**
+>
+> To ensure the stability and reliability of the project, we only optimize and test for specific hardware and software environments during development. This ensures that users deploying and running the project on recommended system configurations will get the best performance with the fewest compatibility issues.
+>
+> By focusing resources on the mainline environment, our team can more efficiently resolve potential bugs and develop new features.
+>
+> In non-mainline environments, due to the diversity of hardware and software configurations, as well as third-party dependency compatibility issues, we cannot guarantee 100% project availability. Therefore, for users who wish to use this project in non-recommended environments, we suggest carefully reading the documentation and FAQ first. Most issues already have corresponding solutions in the FAQ. We also encourage community feedback to help us gradually expand support.
+<table>
+    <tr>
+        <td colspan="3" rowspan="2">Operating System</td>
+    </tr>
+    <tr>
+        <td>Ubuntu 22.04 LTS</td>
+        <td>Windows 10 / 11</td>
+        <td>macOS 11+</td>
+    </tr>
+    <tr>
+        <td colspan="3">CPU</td>
+        <td>x86_64(unsupported ARM Linux)</td>
+        <td>x86_64(unsupported ARM Windows)</td>
+        <td>x86_64 / arm64</td>
+    </tr>
+    <tr>
+        <td colspan="3">Memory</td>
+        <td colspan="3">16GB or more, recommended 32GB+</td>
+    </tr>
+    <tr>
+        <td colspan="3">Python Version</td>
+        <td colspan="3">3.10(Please make sure to create a Python 3.10 virtual environment using conda)</td>
+    </tr>
+    <tr>
+        <td colspan="3">Nvidia Driver Version</td>
+        <td>latest (Proprietary Driver)</td>
+        <td>latest</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td colspan="3">CUDA Environment</td>
+        <td>Automatic installation [12.1 (pytorch) + 11.8 (paddle)]</td>
+        <td>11.8 (manual installation) + cuDNN v8.7.0 (manual installation)</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td rowspan="2">GPU Hardware Support List</td>
+        <td colspan="2">Minimum Requirement 8G+ VRAM</td>
+        <td colspan="2">3060ti/3070/4060<br>
+        8G VRAM enables layout, formula recognition acceleration and OCR acceleration</td>
+        <td rowspan="2">None</td>
+    </tr>
+    <tr>
+        <td colspan="2">Recommended Configuration 10G+ VRAM</td>
+        <td colspan="2">3080/3080ti/3090/3090ti/4070/4070ti/4070tisuper/4080/4090<br>
+        10G VRAM or more can enable layout, formula recognition, OCR acceleration and table recognition acceleration simultaneously
+        </td>
+    </tr>
+</table>
 ### Online Demo
@@ -148,6 +231,7 @@ You can find the `magic-pdf.json` file in your 【user directory】.
 You can modify certain configurations in this file to enable or disable features, such as table recognition:
 > [!NOTE]
 > If the following items are not present in the JSON, please manually add the required items and remove the comment content (standard JSON does not support comments).
@@ -163,15 +247,92 @@ You can modify certain configurations in this file to enable or disable features
        "enable": true  // The formula recognition feature is enabled by default. If you need to disable it, please change the value here to "false".
    },
    "table-config": {
-        "model": "tablemaster",  // When using structEqTable, please change to "struct_eqtable".
+        "model": "rapid_table",  // When using structEqTable, please change to "struct_eqtable".
        "enable": false, // The table recognition feature is disabled by default. If you need to enable it, please change the value here to "true".
        "max_time": 400
    }
 }
 ```
+### Using GPU
+If your device supports CUDA and meets the GPU requirements of the mainline environment, you can use GPU acceleration. Please select the appropriate guide based on your system:
+- [Ubuntu 22.04 LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_en_US.md)
+- [Windows 10/11 + GPU](docs/README_Windows_CUDA_Acceleration_en_US.md)
+- Quick Deployment with Docker
+> [!IMPORTANT]
+> Docker requires a GPU with at least 8GB of VRAM, and all acceleration features are enabled by default.
+>
+> Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker.
+> 
+> ```bash
+> docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
+> ```
+  ```bash
+  wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
+  docker build -t mineru:latest .
+  docker run --rm -it --gpus=all mineru:latest /bin/bash
+  magic-pdf --help
+  ```
 ## Usage
+### Command Line
+```bash
+magic-pdf --help
+Usage: magic-pdf [OPTIONS]
+Options:
+  -v, --version                display the version and exit
+  -p, --path PATH              local pdf filepath or directory  [required]
+  -o, --output-dir PATH        output local directory  [required]
+  -m, --method [ocr|txt|auto]  the method for parsing pdf. ocr: using ocr
+                               technique to extract information from pdf. txt:
+                               suitable for the text-based pdf only and
+                               outperform ocr. auto: automatically choose the
+                               best method for parsing pdf from ocr and txt.
+                               without method specified, auto will be used by
+                               default.
+  -l, --lang TEXT              Input the languages in the pdf (if known) to
+                               improve OCR accuracy.  Optional. You should
+                               input "Abbreviation" with language form url: ht
+                               tps://paddlepaddle.github.io/PaddleOCR/latest/en
+                               /ppocr/blog/multi_languages.html#5-support-languages-
+                               and-abbreviations
+  -d, --debug BOOLEAN          Enables detailed debugging information during
+                               the execution of the CLI commands.
+  -s, --start INTEGER          The starting page for PDF parsing, beginning
+                               from 0.
+  -e, --end INTEGER            The ending page for PDF parsing, beginning from
+                               0.
+  --help                       Show this message and exit.
+## show version
+magic-pdf -v
+## command line example
+magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
+```
+`{some_pdf}` can be a single PDF file or a directory containing multiple PDFs.
+The results will be saved in the `{some_output_dir}` directory. The output file list is as follows:
+```text
+├── some_pdf.md                          # markdown file
+├── images                               # directory for storing images
+├── some_pdf_layout.pdf                  # layout diagram (Include layout reading order)
+├── some_pdf_middle.json                 # MinerU intermediate processing result
+├── some_pdf_model.json                  # model inference result
+├── some_pdf_origin.pdf                  # original PDF file
+├── some_pdf_spans.pdf                   # smallest granularity bbox position information diagram
+└── some_pdf_content_list.json           # Rich text JSON arranged in reading order
+```
+> [!TIP]
+> For more information about the output files, please refer to the [Output File Description](docs/output_file_en_us.md).
 ### API
 Processing files from local disk
@@ -228,6 +389,24 @@ TODO
 - [ ] [Chemical formula recognition](docs/chemical_knowledge_introduction/introduction.pdf)
 - [ ] Geometric shape recognition
+# Known Issues
+- Reading order is determined by the model based on the spatial distribution of readable content, and may be out of order in some areas under extremely complex layouts.
+- Vertical text is not supported.
+- Tables of contents and lists are recognized through rules, and some uncommon list formats may not be recognized.
+- Only one level of headings is supported; hierarchical headings are not currently supported.
+- Code blocks are not yet supported in the layout model.
+- Comic books, art albums, primary school textbooks, and exercises cannot be parsed well.
+- Table recognition may result in row/column recognition errors in complex tables.
+- OCR recognition may produce inaccurate characters in PDFs of lesser-known languages (e.g., diacritical marks in Latin script, easily confused characters in Arabic script).
+- Some formulas may not render correctly in Markdown.
+# FAQ
+[FAQ in Chinese](docs/FAQ_zh_cn.md)
+[FAQ in English](docs/FAQ_en_us.md)
 # All Thanks To Our Contributors
 <a href="https://github.com/opendatalab/MinerU/graphs/contributors">

--- a/README_ja-JP.md
+++ b/README_ja-JP.md
+> [!Warning]
+> このドキュメントはすでに古くなっています。最新版のドキュメントを参照してください：[ENGLISH](README.md)。
 <div id="top">
 <p align="center">
@@ -18,9 +20,7 @@
 <a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 200px; height: 55px;"/></a>
-<div align="center" style="color: red; background-color: #ffdddd; padding: 10px; border: 1px solid red; border-radius: 5px;">
-  <strong>NOTE：</strong> このドキュメントはすでに古くなっています。最新版のドキュメントを参照してください。
-</div>
 [English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)

--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -15,11 +15,12 @@
 [![Downloads](https://static.pepy.tech/badge/magic-pdf/month)](https://pepy.tech/project/magic-pdf)
 [![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://opendatalab.com/OpenSourceTools/Extractor/PDF)
-[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
 [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
+[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/3b3a00a4a0a61577b6c30f989092d20d/mineru_demo.ipynb)
 [![Paper](https://img.shields.io/badge/Paper-arXiv-green)](https://arxiv.org/abs/2409.18839)
 <a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 <!-- language -->
@@ -41,7 +42,7 @@
 </div>
 # 更新记录
+- 2024/11/15 0.9.3发布，为表格识别功能接入了[RapidTable](https://github.com/RapidAI/RapidTable),单表解析速度提升10倍以上，准确率更高，显存占用更低
 - 2024/11/06 0.9.2发布，为表格识别功能接入了[StructTable-InternVL2-1B](https://huggingface.co/U4R/StructTable-InternVL2-1B)模型
 - 2024/10/31 0.9.0发布，这是我们进行了大量代码重构的全新版本，解决了众多问题，提升了性能，降低了硬件需求，并提供了更丰富的易用性：
  - 重构排序模块代码，使用 [layoutreader](https://github.com/ppaanngggg/layoutreader) 进行阅读顺序排序，确保在各种排版下都能实现极高准确率
@@ -76,10 +77,12 @@
            <ul>
            <li><a href="#在线体验">在线体验</a></li>
            <li><a href="#使用CPU快速体验">使用CPU快速体验</a></li>
+            <li><a href="#使用GPU">使用GPU</a></li>
            </ul>
        </li>
        <li><a href="#使用">使用方式</a>
            <ul>
+            <li><a href="#命令行">命令行</a></li>
            <li><a href="#api">API</a></li>
            <li><a href="#部署衍生项目">部署衍生项目</a></li>
            <li><a href="#二次开发">二次开发</a></li>
@@ -111,13 +114,90 @@ MinerU诞生于[书生-浦语](https://github.com/InternLM/InternLM)的预训练
 https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
+## 主要功能
+- 删除页眉、页脚、脚注、页码等元素，确保语义连贯
+- 输出符合人类阅读顺序的文本，适用于单栏、多栏及复杂排版
+- 保留原文档的结构，包括标题、段落、列表等
+- 提取图像、图片描述、表格、表格标题及脚注
+- 自动识别并转换文档中的公式为LaTeX格式
+- 自动识别并转换文档中的表格为LaTeX或HTML格式
+- 自动检测扫描版PDF和乱码PDF，并启用OCR功能
+- OCR支持84种语言的检测与识别
+- 支持多种输出格式，如多模态与NLP的Markdown、按阅读顺序排序的JSON、含有丰富信息的中间格式等
+- 支持多种可视化结果，包括layout可视化、span可视化等，便于高效确认输出效果与质检
+- 支持CPU和GPU环境
+- 兼容Windows、Linux和Mac平台
 ## 快速开始
-有多种不同方式可以体验MinerU的效果：
+如果遇到任何安装问题，请先查询 <a href="#faq">FAQ</a> </br>
+如果遇到解析效果不及预期，参考 <a href="#known-issues">Known Issues</a></br>
+有3种不同方式可以体验MinerU的效果：
 - [在线体验(无需任何安装)](#在线体验)
 - [使用CPU快速体验（Windows，Linux，Mac）](#使用cpu快速体验)
+- [Linux/Windows + CUDA](#使用gpu)
+> [!WARNING]
+> **安装前必看——软硬件环境支持说明**
+> 
+> 为了确保项目的稳定性和可靠性，我们在开发过程中仅对特定的软硬件环境进行优化和测试。这样当用户在推荐的系统配置上部署和运行项目时，能够获得最佳的性能表现和最少的兼容性问题。
+>
+> 通过集中资源和精力于主线环境，我们团队能够更高效地解决潜在的BUG，及时开发新功能。
+>
+> 在非主线环境中，由于硬件、软件配置的多样性，以及第三方依赖项的兼容性问题，我们无法100%保证项目的完全可用性。因此，对于希望在非推荐环境中使用本项目的用户，我们建议先仔细阅读文档以及FAQ，大多数问题已经在FAQ中有对应的解决方案，除此之外我们鼓励社区反馈问题，以便我们能够逐步扩大支持范围。
+<table>
+    <tr>
+        <td colspan="3" rowspan="2">操作系统</td>
+    </tr>
+    <tr>
+        <td>Ubuntu 22.04 LTS</td>
+        <td>Windows 10 / 11</td>
+        <td>macOS 11+</td>
+    </tr>
+    <tr>
+        <td colspan="3">CPU</td>
+        <td>x86_64(暂不支持ARM Linux)</td>
+        <td>x86_64(暂不支持ARM Windows)</td>
+        <td>x86_64 / arm64</td>
+    </tr>
+    <tr>
+        <td colspan="3">内存</td>
+        <td colspan="3">大于等于16GB，推荐32G以上</td>
+    </tr>
+    <tr>
+        <td colspan="3">python版本</td>
+        <td colspan="3">3.10 (请务必通过conda创建3.10虚拟环境)</td>
+    </tr>
+    <tr>
+        <td colspan="3">Nvidia Driver 版本</td>
+        <td>latest(专有驱动)</td>
+        <td>latest</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td colspan="3">CUDA环境</td>
+        <td>自动安装[12.1(pytorch)+11.8(paddle)]</td>
+        <td>11.8(手动安装)+cuDNN v8.7.0(手动安装)</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td rowspan="2">GPU硬件支持列表</td>
+        <td colspan="2">最低要求 8G+显存</td>
+        <td colspan="2">3060ti/3070/4060<br>
+        8G显存可开启全部加速功能(表格仅限rapid_table)</td>
+        <td rowspan="2">None</td>
+    </tr>
+    <tr>
+        <td colspan="2">推荐配置 10G+显存</td>
+        <td colspan="2">3080/3080ti/3090/3090ti/4070/4070ti/4070tisuper/4080/4090<br>
+        10G显存及以上可开启全部加速功能<br>
+        </td>
+    </tr>
+</table>
 ### 在线体验
 稳定版(经过QA验证的稳定版本)：
@@ -171,16 +251,94 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
        "enable": true  // 公式识别功能默认是开启的，如果需要关闭请修改此处的值为"false"
    },
    "table-config": {
-        "model": "tablemaster",  // 使用structEqTable请修改为"struct_eqtable"
+        "model": "rapid_table",  // 使用structEqTable请修改为"struct_eqtable"
        "enable": false, // 表格识别功能默认是关闭的，如果需要开启请修改此处的值为"true"
        "max_time": 400
    }
 }
 ```
+### 使用GPU
+如果您的设备支持CUDA，且满足主线环境中的显卡要求，则可以使用GPU加速，请根据自己的系统选择适合的教程：
+- [Ubuntu22.04LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md)
+- [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
+- 使用Docker快速部署
+> [!IMPORTANT]
+> Docker 需设备gpu显存大于等于8GB，默认开启所有加速功能
+> 
+> 运行本docker前可以通过以下命令检测自己的设备是否支持在docker上使用CUDA加速
+> 
+> ```bash
+> docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
+> ```
+  ```bash
+  wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
+  docker build -t mineru:latest .
+  docker run --rm -it --gpus=all mineru:latest /bin/bash
+  magic-pdf --help
+  ```
 ## 使用
+### 命令行
+```bash
+magic-pdf --help
+Usage: magic-pdf [OPTIONS]
+Options:
+  -v, --version                display the version and exit
+  -p, --path PATH              local pdf filepath or directory  [required]
+  -o, --output-dir PATH        output local directory  [required]
+  -m, --method [ocr|txt|auto]  the method for parsing pdf. ocr: using ocr
+                               technique to extract information from pdf. txt:
+                               suitable for the text-based pdf only and
+                               outperform ocr. auto: automatically choose the
+                               best method for parsing pdf from ocr and txt.
+                               without method specified, auto will be used by
+                               default.
+  -l, --lang TEXT              Input the languages in the pdf (if known) to
+                               improve OCR accuracy.  Optional. You should
+                               input "Abbreviation" with language form url: ht
+                               tps://paddlepaddle.github.io/PaddleOCR/latest/en
+                               /ppocr/blog/multi_languages.html#5-support-languages-
+                               and-abbreviations
+  -d, --debug BOOLEAN          Enables detailed debugging information during
+                               the execution of the CLI commands.
+  -s, --start INTEGER          The starting page for PDF parsing, beginning
+                               from 0.
+  -e, --end INTEGER            The ending page for PDF parsing, beginning from
+                               0.
+  --help                       Show this message and exit.
+## show version
+magic-pdf -v
+## command line example
+magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
+```
+其中 `{some_pdf}` 可以是单个pdf文件，也可以是一个包含多个pdf文件的目录。
+运行完命令后输出的结果会保存在`{some_output_dir}`目录下, 输出的文件列表如下
+```text
+├── some_pdf.md                          # markdown 文件
+├── images                               # 存放图片目录
+├── some_pdf_layout.pdf                  # layout 绘图 （包含layout阅读顺序）
+├── some_pdf_middle.json                 # minerU 中间处理结果
+├── some_pdf_model.json                  # 模型推理结果
+├── some_pdf_origin.pdf                  # 原 pdf 文件
+├── some_pdf_spans.pdf                   # 最小粒度的bbox位置信息绘图
+└── some_pdf_content_list.json           # 按阅读顺序排列的富文本json
+```
+> [!TIP]
+> 更多有关输出文件的信息，请参考[输出文件说明](docs/output_file_zh_cn.md)
 ### API
 处理本地磁盘上的文件
@@ -237,6 +395,24 @@ TODO
 - [ ] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)
 - [ ] 几何图形识别
+# Known Issues
+- 阅读顺序基于模型对可阅读内容在空间中的分布进行排序，在极端复杂的排版下可能会部分区域乱序
+- 不支持竖排文字
+- 目录和列表通过规则进行识别，少部分不常见的列表形式可能无法识别
+- 标题只有一级，目前不支持标题分级
+- 代码块在layout模型里还没有支持
+- 漫画书、艺术图册、小学教材、习题尚不能很好解析
+- 表格识别在复杂表格上可能会出现行/列识别错误
+- 在小语种PDF上，OCR识别可能会出现字符不准确的情况（如拉丁文的重音符号、阿拉伯文易混淆字符等）
+- 部分公式可能会无法在markdown中渲染
+# FAQ
+[常见问题](docs/FAQ_zh_cn.md)
+[FAQ](docs/FAQ_en_us.md)
 # All Thanks To Our Contributors
@@ -253,6 +429,7 @@ TODO
 # Acknowledgments
 - [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
+- [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO)
 - [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)
 - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
 - [PyMuPDF](https://github.com/pymupdf/PyMuPDF)

--- a/demo/magic_pdf_parse_main.py
+++ b/demo/magic_pdf_parse_main.py
@@ -19,9 +19,10 @@ def json_md_dump(
        pdf_name,
        content_list,
        md_content,
+        orig_model_list,
 ):
    # 写入模型结果到 model.json
-    orig_model_list = copy.deepcopy(pipe.model_list)
    md_writer.write(
        content=json.dumps(orig_model_list, ensure_ascii=False, indent=4),
        path=f"{pdf_name}_model.json"
@@ -87,9 +88,12 @@ def pdf_parse_main(
        pdf_bytes = open(pdf_path, "rb").read()  # 读取 pdf 文件的二进制数据
+        orig_model_list = []
        if model_json_path:
            # 读取已经被模型解析后的pdf文件的 json 原始数据，list 类型
            model_json = json.loads(open(model_json_path, "r", encoding="utf-8").read())
+            orig_model_list = copy.deepcopy(model_json)
        else:
            model_json = []
@@ -115,8 +119,9 @@ def pdf_parse_main(
        pipe.pipe_classify()
        # 如果没有传入模型数据，则使用内置模型解析
-        if not model_json:
+        if len(model_json) == 0:
            pipe.pipe_analyze()  # 解析
+            orig_model_list = copy.deepcopy(pipe.model_list)
        # 执行解析
        pipe.pipe_parse()
@@ -126,7 +131,7 @@ def pdf_parse_main(
        md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode="none")
        if is_json_md_dump:
-            json_md_dump(pipe, md_writer, pdf_name, content_list, md_content)
+            json_md_dump(pipe, md_writer, pdf_name, content_list, md_content, orig_model_list)
        if is_draw_visualization_bbox:
            draw_visualization_bbox(pipe.pdf_mid_data['pdf_info'], pdf_bytes, output_path, pdf_name)

--- a/docs/download_models.py
+++ b/docs/download_models.py
-import json
-import os
-import requests
-from modelscope import snapshot_download
-def download_json(url):
-    # 下载JSON文件
-    response = requests.get(url)
-    response.raise_for_status()  # 检查请求是否成功
-    return response.json()
-def download_and_modify_json(url, local_filename, modifications):
-    if os.path.exists(local_filename):
-        data = json.load(open(local_filename))
-        config_version = data.get('config_version', '0.0.0')
-        if config_version < '1.0.0':
-            data = download_json(url)
-    else:
-        data = download_json(url)
-    # 修改内容
-    for key, value in modifications.items():
-        data[key] = value
-    # 保存修改后的内容
-    with open(local_filename, 'w', encoding='utf-8') as f:
-        json.dump(data, f, ensure_ascii=False, indent=4)
-if __name__ == '__main__':
-    mineru_patterns = [
-        "models/Layout/LayoutLMv3/*",
-        "models/Layout/YOLO/*",
-        "models/MFD/YOLO/*",
-        "models/MFR/unimernet_small/*",
-        "models/TabRec/TableMaster/*",
-        "models/TabRec/StructEqTable/*",
-    ]
-    model_dir = snapshot_download('opendatalab/PDF-Extract-Kit-1.0', allow_patterns=mineru_patterns)
-    layoutreader_model_dir = snapshot_download('ppaanngggg/layoutreader')
-    model_dir = model_dir + '/models'
-    print(f'model_dir is: {model_dir}')
-    print(f'layoutreader_model_dir is: {layoutreader_model_dir}')
-    json_url = 'https://gitee.com/myhloli/MinerU/raw/dev/magic-pdf.template.json'
-    config_file_name = 'magic-pdf.json'
-    home_dir = os.path.expanduser('~')
-    config_file = os.path.join(home_dir, config_file_name)
-    json_mods = {
-        'models-dir': model_dir,
-        'layoutreader-model-dir': layoutreader_model_dir,
-    }
-    download_and_modify_json(json_url, config_file, json_mods)
-    print(f'The configuration file has been configured successfully, the path is: {config_file}')
--- a/docs/download_models_hf.py
+++ b/docs/download_models_hf.py
-import json
-import os
-import requests
-from huggingface_hub import snapshot_download
-def download_json(url):
-    # 下载JSON文件
-    response = requests.get(url)
-    response.raise_for_status()  # 检查请求是否成功
-    return response.json()
-def download_and_modify_json(url, local_filename, modifications):
-    if os.path.exists(local_filename):
-        data = json.load(open(local_filename))
-        config_version = data.get('config_version', '0.0.0')
-        if config_version < '1.0.0':
-            data = download_json(url)
-    else:
-        data = download_json(url)
-    # 修改内容
-    for key, value in modifications.items():
-        data[key] = value
-    # 保存修改后的内容
-    with open(local_filename, 'w', encoding='utf-8') as f:
-        json.dump(data, f, ensure_ascii=False, indent=4)
-if __name__ == '__main__':
-    mineru_patterns = [
-        "models/Layout/LayoutLMv3/*",
-        "models/Layout/YOLO/*",
-        "models/MFD/YOLO/*",
-        "models/MFR/unimernet_small/*",
-        "models/TabRec/TableMaster/*",
-        "models/TabRec/StructEqTable/*",
-    ]
-    model_dir = snapshot_download('opendatalab/PDF-Extract-Kit-1.0', allow_patterns=mineru_patterns)
-    layoutreader_pattern = [
-        "*.json",
-        "*.safetensors",
-    ]
-    layoutreader_model_dir = snapshot_download('hantian/layoutreader', allow_patterns=layoutreader_pattern)
-    model_dir = model_dir + '/models'
-    print(f'model_dir is: {model_dir}')
-    print(f'layoutreader_model_dir is: {layoutreader_model_dir}')
-    json_url = 'https://github.com/opendatalab/MinerU/raw/dev/magic-pdf.template.json'
-    config_file_name = 'magic-pdf.json'
-    home_dir = os.path.expanduser('~')
-    config_file = os.path.join(home_dir, config_file_name)
-    json_mods = {
-        'models-dir': model_dir,
-        'layoutreader-model-dir': layoutreader_model_dir,
-    }
-    download_and_modify_json(json_url, config_file, json_mods)
-    print(f'The configuration file has been configured successfully, the path is: {config_file}')
--- a/docs/how_to_download_models_en.md
+++ b/docs/how_to_download_models_en.md
@@ -8,7 +8,7 @@ Use a Python Script to Download Model Files from Hugging Face
 ```bash
 pip install huggingface_hub
-wget https://github.com/opendatalab/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
+wget https://github.com/opendatalab/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
 python download_models_hf.py
 ```

--- a/docs/how_to_download_models_zh_cn.md
+++ b/docs/how_to_download_models_zh_cn.md
@@ -8,7 +8,7 @@
  <summary>方法一：从 Hugging Face 下载模型</summary>
  <p>使用python脚本 从Hugging Face下载模型文件</p>
  <pre><code>pip install huggingface_hub
-wget https://gitee.com/myhloli/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
+wget https://gitee.com/myhloli/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
 python download_models_hf.py</code></pre>
 </details>
@@ -18,7 +18,7 @@ python download_models_hf.py</code></pre>
 ```bash
 pip install modelscope
-wget https://gitee.com/myhloli/MinerU/raw/master/docs/download_models.py -O download_models.py
+wget https://gitee.com/myhloli/MinerU/raw/master/scripts/download_models.py -O download_models.py
 python download_models.py
 ```

--- a/magic-pdf.template.json
+++ b/magic-pdf.template.json
@@ -15,7 +15,7 @@
        "enable": true
    },
    "table-config": {
-        "model": "tablemaster",
+        "model": "rapid_table",
        "enable": false,
        "max_time": 400
    },

--- a/magic_pdf/dict2md/ocr_mkcontent.py
+++ b/magic_pdf/dict2md/ocr_mkcontent.py
@@ -168,7 +168,7 @@ def merge_para_with_text(para_block):
                        # 如果是前一行带有-连字符，那么末尾不应该加空格
                        if __is_hyphen_at_line_end(content):
                            para_text += content[:-1]
-                        elif len(content) == 1 and content not in ['A', 'I', 'a', 'i']:
+                        elif len(content) == 1 and content not in ['A', 'I', 'a', 'i'] and not content.isdigit():
                            para_text += content
                        else:  # 西方文本语境下 content间需要空格分隔
                            para_text += f"{content} "

--- a/magic_pdf/libs/Constants.py
+++ b/magic_pdf/libs/Constants.py
@@ -50,4 +50,6 @@ class MODEL_NAME:
    YOLO_V8_MFD = "yolo_v8_mfd"
    UniMerNet_v2_Small = "unimernet_small"
\ No newline at end of file
+    RAPID_TABLE = "rapid_table"
\ No newline at end of file
--- a/magic_pdf/libs/config_reader.py
+++ b/magic_pdf/libs/config_reader.py
@@ -92,7 +92,7 @@ def get_table_recog_config():
    table_config = config.get('table-config')
    if table_config is None:
        logger.warning(f"'table-config' not found in {CONFIG_FILE_NAME}, use 'False' as default")
-        return json.loads(f'{{"model": "{MODEL_NAME.TABLE_MASTER}","enable": false, "max_time": 400}}')
+        return json.loads(f'{{"model": "{MODEL_NAME.RAPID_TABLE}","enable": false, "max_time": 400}}')
    else:
        return table_config

--- a/magic_pdf/model/pdf_extract_kit.py
+++ b/magic_pdf/model/pdf_extract_kit.py
+import numpy as np
+import torch
 from loguru import logger
 import os
 import time
-from pathlib import Path
+import cv2
-import shutil
+import yaml
-from magic_pdf.libs.Constants import *
+from PIL import Image
-from magic_pdf.libs.clean_memory import clean_memory
-from magic_pdf.model.model_list import AtomicModel
 os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
 os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
 try:
-    import cv2
-    import yaml
-    import argparse
-    import numpy as np
-    import torch
    import torchtext
    if torchtext.__version__ >= "0.18.0":
        torchtext.disable_torchtext_deprecation_warning()
-    from PIL import Image
+except ImportError:
-    from torchvision import transforms
+    pass
-    from torch.utils.data import Dataset, DataLoader
-    from ultralytics import YOLO
-    from unimernet.common.config import Config
-    import unimernet.tasks as tasks
-    from unimernet.processors import load_processor
-    from doclayout_yolo import YOLOv10
-except ImportError as e:
-    logger.exception(e)
-    logger.error(
-        'Required dependency not installed, please install by \n'
-        '"pip install magic-pdf[full] --extra-index-url https://myhloli.github.io/wheels/"')
-    exit(1)
-from magic_pdf.model.pek_sub_modules.layoutlmv3.model_init import Layoutlmv3_Predictor
-from magic_pdf.model.pek_sub_modules.post_process import latex_rm_whitespace
-from magic_pdf.model.pek_sub_modules.self_modify import ModifiedPaddleOCR
-from magic_pdf.model.pek_sub_modules.structeqtable.StructTableModel import StructTableModel
-from magic_pdf.model.ppTableModel import ppTableModel
-def table_model_init(table_model_type, model_path, max_time, _device_='cpu'):
-    if table_model_type == MODEL_NAME.STRUCT_EQTABLE:
-        table_model = StructTableModel(model_path, max_time=max_time)
-    elif table_model_type == MODEL_NAME.TABLE_MASTER:
-        config = {
-            "model_dir": model_path,
-            "device": _device_
-        }
-        table_model = ppTableModel(config)
-    else:
-        logger.error("table model type not allow")
-        exit(1)
-    return table_model
-def mfd_model_init(weight):
-    mfd_model = YOLO(weight)
-    return mfd_model
-def mfr_model_init(weight_dir, cfg_path, _device_='cpu'):
-    args = argparse.Namespace(cfg_path=cfg_path, options=None)
-    cfg = Config(args)
-    cfg.config.model.pretrained = os.path.join(weight_dir, "pytorch_model.pth")
-    cfg.config.model.model_config.model_name = weight_dir
-    cfg.config.model.tokenizer_config.path = weight_dir
-    task = tasks.setup_task(cfg)
-    model = task.build_model(cfg)
-    model.to(_device_)
-    model.eval()
-    vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)
-    mfr_transform = transforms.Compose([vis_processor, ])
-    return [model, mfr_transform]
-def layout_model_init(weight, config_file, device):
-    model = Layoutlmv3_Predictor(weight, config_file, device)
-    return model
-def doclayout_yolo_model_init(weight):
-    model = YOLOv10(weight)
-    return model
-def ocr_model_init(show_log: bool = False, det_db_box_thresh=0.3, lang=None, use_dilation=True, det_db_unclip_ratio=1.8):
-    if lang is not None:
-        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh, lang=lang, use_dilation=use_dilation, det_db_unclip_ratio=det_db_unclip_ratio)
-    else:
-        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh, use_dilation=use_dilation, det_db_unclip_ratio=det_db_unclip_ratio)
-    return model
-class MathDataset(Dataset):
-    def __init__(self, image_paths, transform=None):
-        self.image_paths = image_paths
-        self.transform = transform
-    def __len__(self):
-        return len(self.image_paths)
-    def __getitem__(self, idx):
-        # if not pil image, then convert to pil image
-        if isinstance(self.image_paths[idx], str):
-            raw_image = Image.open(self.image_paths[idx])
-        else:
-            raw_image = self.image_paths[idx]
-        if self.transform:
-            image = self.transform(raw_image)
-            return image
-class AtomModelSingleton:
-    _instance = None
-    _models = {}
-    def __new__(cls, *args, **kwargs):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-    def get_atom_model(self, atom_model_name: str, **kwargs):
-        lang = kwargs.get("lang", None)
-        layout_model_name = kwargs.get("layout_model_name", None)
-        key = (atom_model_name, layout_model_name, lang)
-        if key not in self._models:
-            self._models[key] = atom_model_init(model_name=atom_model_name, **kwargs)
-        return self._models[key]
-def atom_model_init(model_name: str, **kwargs):
-    if model_name == AtomicModel.Layout:
-        if kwargs.get("layout_model_name") == MODEL_NAME.LAYOUTLMv3:
-            atom_model = layout_model_init(
-                kwargs.get("layout_weights"),
-                kwargs.get("layout_config_file"),
-                kwargs.get("device")
-            )
-        elif kwargs.get("layout_model_name") == MODEL_NAME.DocLayout_YOLO:
-            atom_model = doclayout_yolo_model_init(
-                kwargs.get("doclayout_yolo_weights"),
-            )
-    elif model_name == AtomicModel.MFD:
-        atom_model = mfd_model_init(
-            kwargs.get("mfd_weights")
-        )
-    elif model_name == AtomicModel.MFR:
-        atom_model = mfr_model_init(
-            kwargs.get("mfr_weight_dir"),
-            kwargs.get("mfr_cfg_path"),
-            kwargs.get("device")
-        )
-    elif model_name == AtomicModel.OCR:
-        atom_model = ocr_model_init(
-            kwargs.get("ocr_show_log"),
-            kwargs.get("det_db_box_thresh"),
-            kwargs.get("lang")
-        )
-    elif model_name == AtomicModel.Table:
-        atom_model = table_model_init(
-            kwargs.get("table_model_name"),
-            kwargs.get("table_model_path"),
-            kwargs.get("table_max_time"),
-            kwargs.get("device")
-        )
-    else:
-        logger.error("model name not allow")
-        exit(1)
-    return atom_model
-#  Unified crop img logic
+from magic_pdf.libs.Constants import *
-def crop_img(input_res, input_pil_img, crop_paste_x=0, crop_paste_y=0):
+from magic_pdf.model.model_list import AtomicModel
-    crop_xmin, crop_ymin = int(input_res['poly'][0]), int(input_res['poly'][1])
+from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
-    crop_xmax, crop_ymax = int(input_res['poly'][4]), int(input_res['poly'][5])
+from magic_pdf.model.sub_modules.model_utils import get_res_list_from_layout_res, crop_img, clean_vram
-    # Create a white background with an additional width and height of 50
+from magic_pdf.model.sub_modules.ocr.paddleocr.ocr_utils import get_adjusted_mfdetrec_res, get_ocr_result_list
-    crop_new_width = crop_xmax - crop_xmin + crop_paste_x * 2
-    crop_new_height = crop_ymax - crop_ymin + crop_paste_y * 2
-    return_image = Image.new('RGB', (crop_new_width, crop_new_height), 'white')
-    # Crop image
-    crop_box = (crop_xmin, crop_ymin, crop_xmax, crop_ymax)
-    cropped_img = input_pil_img.crop(crop_box)
-    return_image.paste(cropped_img, (crop_paste_x, crop_paste_y))
-    return_list = [crop_paste_x, crop_paste_y, crop_xmin, crop_ymin, crop_xmax, crop_ymax, crop_new_width, crop_new_height]
-    return return_image, return_list
 class CustomPEKModel:
@@ -226,7 +59,7 @@ class CustomPEKModel:
        self.table_config = kwargs.get("table_config")
        self.apply_table = self.table_config.get("enable", False)
        self.table_max_time = self.table_config.get("max_time", TABLE_MAX_TIME_VALUE)
-        self.table_model_name = self.table_config.get("model", MODEL_NAME.TABLE_MASTER)
+        self.table_model_name = self.table_config.get("model", MODEL_NAME.RAPID_TABLE)
        # ocr config
        self.apply_ocr = ocr
@@ -235,7 +68,8 @@ class CustomPEKModel:
        logger.info(
            "DocAnalysis init, this may take some times, layout_model: {}, apply_formula: {}, apply_ocr: {}, "
            "apply_table: {}, table_model: {}, lang: {}".format(
-                self.layout_model_name, self.apply_formula, self.apply_ocr, self.apply_table, self.table_model_name, self.lang
+                self.layout_model_name, self.apply_formula, self.apply_ocr, self.apply_table, self.table_model_name,
+                self.lang
            )
        )
        # 初始化解析方案
@@ -248,17 +82,17 @@ class CustomPEKModel:
        # 初始化公式识别
        if self.apply_formula:
            # 初始化公式检测模型
            self.mfd_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.MFD,
-                mfd_weights=str(os.path.join(models_dir, self.configs["weights"][self.mfd_model_name]))
+                mfd_weights=str(os.path.join(models_dir, self.configs["weights"][self.mfd_model_name])),
+                device=self.device
            )
            # 初始化公式解析模型
            mfr_weight_dir = str(os.path.join(models_dir, self.configs["weights"][self.mfr_model_name]))
            mfr_cfg_path = str(os.path.join(model_config_dir, "UniMERNet", "demo.yaml"))
-            self.mfr_model, self.mfr_transform = atom_model_manager.get_atom_model(
+            self.mfr_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.MFR,
                mfr_weight_dir=mfr_weight_dir,
                mfr_cfg_path=mfr_cfg_path,
@@ -278,7 +112,8 @@ class CustomPEKModel:
            self.layout_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.Layout,
                layout_model_name=MODEL_NAME.DocLayout_YOLO,
-                doclayout_yolo_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name]))
+                doclayout_yolo_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name])),
+                device=self.device
            )
        # 初始化ocr
        if self.apply_ocr:
@@ -305,26 +140,15 @@ class CustomPEKModel:
        page_start = time.time()
-        latex_filling_list = []
-        mf_image_list = []
        # layout检测
        layout_start = time.time()
+        layout_res = []
        if self.layout_model_name == MODEL_NAME.LAYOUTLMv3:
            # layoutlmv3
            layout_res = self.layout_model(image, ignore_catids=[])
        elif self.layout_model_name == MODEL_NAME.DocLayout_YOLO:
            # doclayout_yolo
-            layout_res = []
+            layout_res = self.layout_model.predict(image)
-            doclayout_yolo_res = self.layout_model.predict(image, imgsz=1024, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
-            for xyxy, conf, cla in zip(doclayout_yolo_res.boxes.xyxy.cpu(), doclayout_yolo_res.boxes.conf.cpu(), doclayout_yolo_res.boxes.cls.cpu()):
-                xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
-                new_item = {
-                    'category_id': int(cla.item()),
-                    'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
-                    'score': round(float(conf.item()), 3),
-                }
-                layout_res.append(new_item)
        layout_cost = round(time.time() - layout_start, 2)
        logger.info(f"layout detection time: {layout_cost}")
@@ -333,59 +157,21 @@ class CustomPEKModel:
        if self.apply_formula:
            # 公式检测
            mfd_start = time.time()
-            mfd_res = self.mfd_model.predict(image, imgsz=1888, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
+            mfd_res = self.mfd_model.predict(image)
            logger.info(f"mfd time: {round(time.time() - mfd_start, 2)}")
-            for xyxy, conf, cla in zip(mfd_res.boxes.xyxy.cpu(), mfd_res.boxes.conf.cpu(), mfd_res.boxes.cls.cpu()):
-                xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
-                new_item = {
-                    'category_id': 13 + int(cla.item()),
-                    'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
-                    'score': round(float(conf.item()), 2),
-                    'latex': '',
-                }
-                layout_res.append(new_item)
-                latex_filling_list.append(new_item)
-                bbox_img = pil_img.crop((xmin, ymin, xmax, ymax))
-                mf_image_list.append(bbox_img)
            # 公式识别
            mfr_start = time.time()
-            dataset = MathDataset(mf_image_list, transform=self.mfr_transform)
+            formula_list = self.mfr_model.predict(mfd_res, image)
-            dataloader = DataLoader(dataset, batch_size=64, num_workers=0)
+            layout_res.extend(formula_list)
-            mfr_res = []
-            for mf_img in dataloader:
-                mf_img = mf_img.to(self.device)
-                with torch.no_grad():
-                    output = self.mfr_model.generate({'image': mf_img})
-                mfr_res.extend(output['pred_str'])
-            for res, latex in zip(latex_filling_list, mfr_res):
-                res['latex'] = latex_rm_whitespace(latex)
            mfr_cost = round(time.time() - mfr_start, 2)
-            logger.info(f"formula nums: {len(mf_image_list)}, mfr time: {mfr_cost}")
+            logger.info(f"formula nums: {len(formula_list)}, mfr time: {mfr_cost}")
-        # Select regions for OCR / formula regions / table regions
+        # 清理显存
-        ocr_res_list = []
+        clean_vram(self.device, vram_threshold=8)
-        table_res_list = []
-        single_page_mfdetrec_res = []
+        # 从layout_res中获取ocr区域、表格区域、公式区域
-        for res in layout_res:
+        ocr_res_list, table_res_list, single_page_mfdetrec_res = get_res_list_from_layout_res(layout_res)
-            if int(res['category_id']) in [13, 14]:
-                single_page_mfdetrec_res.append({
-                    "bbox": [int(res['poly'][0]), int(res['poly'][1]),
-                             int(res['poly'][4]), int(res['poly'][5])],
-                })
-            elif int(res['category_id']) in [0, 1, 2, 4, 6, 7]:
-                ocr_res_list.append(res)
-            elif int(res['category_id']) in [5]:
-                table_res_list.append(res)
-        if torch.cuda.is_available() and self.device != 'cpu':
-            properties = torch.cuda.get_device_properties(self.device)
-            total_memory = properties.total_memory / (1024 ** 3)  # 将字节转换为 GB
-            if total_memory <= 10:
-                gc_start = time.time()
-                clean_memory()
-                gc_time = round(time.time() - gc_start, 2)
-                logger.info(f"gc time: {gc_time}")
        # ocr识别
        if self.apply_ocr:
@@ -393,23 +179,7 @@ class CustomPEKModel:
            # Process each area that requires OCR processing
            for res in ocr_res_list:
                new_image, useful_list = crop_img(res, pil_img, crop_paste_x=50, crop_paste_y=50)
-                paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
+                adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list)
-                # Adjust the coordinates of the formula area
-                adjusted_mfdetrec_res = []
-                for mf_res in single_page_mfdetrec_res:
-                    mf_xmin, mf_ymin, mf_xmax, mf_ymax = mf_res["bbox"]
-                    # Adjust the coordinates of the formula area to the coordinates relative to the cropping area
-                    x0 = mf_xmin - xmin + paste_x
-                    y0 = mf_ymin - ymin + paste_y
-                    x1 = mf_xmax - xmin + paste_x
-                    y1 = mf_ymax - ymin + paste_y
-                    # Filter formula blocks outside the graph
-                    if any([x1 < 0, y1 < 0]) or any([x0 > new_width, y0 > new_height]):
-                        continue
-                    else:
-                        adjusted_mfdetrec_res.append({
-                            "bbox": [x0, y0, x1, y1],
-                        })
                # OCR recognition
                new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
@@ -417,22 +187,8 @@ class CustomPEKModel:
                # Integration results
                if ocr_res:
-                    for box_ocr_res in ocr_res:
+                    ocr_result_list = get_ocr_result_list(ocr_res, useful_list)
-                        p1, p2, p3, p4 = box_ocr_res[0]
+                    layout_res.extend(ocr_result_list)
-                        text, score = box_ocr_res[1]
-                        # Convert the coordinates back to the original coordinate system
-                        p1 = [p1[0] - paste_x + xmin, p1[1] - paste_y + ymin]
-                        p2 = [p2[0] - paste_x + xmin, p2[1] - paste_y + ymin]
-                        p3 = [p3[0] - paste_x + xmin, p3[1] - paste_y + ymin]
-                        p4 = [p4[0] - paste_x + xmin, p4[1] - paste_y + ymin]
-                        layout_res.append({
-                            'category_id': 15,
-                            'poly': p1 + p2 + p3 + p4,
-                            'score': round(score, 2),
-                            'text': text,
-                        })
            ocr_cost = round(time.time() - ocr_start, 2)
            logger.info(f"ocr time: {ocr_cost}")
@@ -443,41 +199,30 @@ class CustomPEKModel:
            for res in table_res_list:
                new_image, _ = crop_img(res, pil_img)
                single_table_start_time = time.time()
-                # logger.info("------------------table recognition processing begins-----------------")
-                latex_code = None
                html_code = None
                if self.table_model_name == MODEL_NAME.STRUCT_EQTABLE:
                    with torch.no_grad():
                        table_result = self.table_model.predict(new_image, "html")
                        if len(table_result) > 0:
                            html_code = table_result[0]
-                else:
+                elif self.table_model_name == MODEL_NAME.TABLE_MASTER:
                    html_code = self.table_model.img2html(new_image)
+                elif self.table_model_name == MODEL_NAME.RAPID_TABLE:
+                    html_code, table_cell_bboxes, elapse = self.table_model.predict(new_image)
                run_time = time.time() - single_table_start_time
-                # logger.info(f"------------table recognition processing ends within {run_time}s-----")
                if run_time > self.table_max_time:
-                    logger.warning(f"------------table recognition processing exceeds max time {self.table_max_time}s----------")
+                    logger.warning(f"table recognition processing exceeds max time {self.table_max_time}s")
                # 判断是否返回正常
+                if html_code:
-                if latex_code:
-                    expected_ending = latex_code.strip().endswith('end{tabular}') or latex_code.strip().endswith('end{table}')
-                    if expected_ending:
-                        res["latex"] = latex_code
-                    else:
-                        logger.warning(f"table recognition processing fails, not found expected LaTeX table end")
-                elif html_code:
                    expected_ending = html_code.strip().endswith('</html>') or html_code.strip().endswith('</table>')
                    if expected_ending:
                        res["html"] = html_code
                    else:
                        logger.warning(f"table recognition processing fails, not found expected HTML table end")
                else:
-                    logger.warning(f"table recognition processing fails, not get latex or html return")
+                    logger.warning(f"table recognition processing fails, not get html return")
            logger.info(f"table time: {round(time.time() - table_start, 2)}")
        logger.info(f"-----page total time: {round(time.time() - page_start, 2)}-----")
        return layout_res
--- a/magic_pdf/model/pek_sub_modules/post_process.py
+++ b/magic_pdf/model/pek_sub_modules/post_process.py
-import re
-def layout_rm_equation(layout_res):
-    rm_idxs = []
-    for idx, ele in enumerate(layout_res['layout_dets']):
-        if ele['category_id'] == 10:
-            rm_idxs.append(idx)
-    for idx in rm_idxs[::-1]:
-        del layout_res['layout_dets'][idx]
-    return layout_res
-def get_croped_image(image_pil, bbox):
-    x_min, y_min, x_max, y_max = bbox
-    croped_img = image_pil.crop((x_min, y_min, x_max, y_max))
-    return croped_img
-def latex_rm_whitespace(s: str):
-    """Remove unnecessary whitespace from LaTeX code.
-    """
-    text_reg = r'(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})'
-    letter = '[a-zA-Z]'
-    noletter = '[\W_^\d]'
-    names = [x[0].replace(' ', '') for x in re.findall(text_reg, s)]
-    s = re.sub(text_reg, lambda match: str(names.pop(0)), s)
-    news = s
-    while True:
-        s = news
-        news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, noletter), r'\1\2', s)
-        news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, letter), r'\1\2', news)
-        news = re.sub(r'(%s)\s+?(%s)' % (letter, noletter), r'\1\2', news)
-        if news == s:
-            break
-    return s
\ No newline at end of file
--- a/magic_pdf/model/pek_sub_modules/__init__.py
+++ b/magic_pdf/model/pek_sub_modules/__init__.py
--- a/magic_pdf/model/pek_sub_modules/layoutlmv3/__init__.py
+++ b/magic_pdf/model/pek_sub_modules/layoutlmv3/__init__.py
--- a/magic_pdf/model/sub_modules/layout/doclayout_yolo/DocLayoutYOLO.py
+++ b/magic_pdf/model/sub_modules/layout/doclayout_yolo/DocLayoutYOLO.py
+from doclayout_yolo import YOLOv10
+class DocLayoutYOLOModel(object):
+    def __init__(self, weight, device):
+        self.model = YOLOv10(weight)
+        self.device = device
+    def predict(self, image):
+        layout_res = []
+        doclayout_yolo_res = self.model.predict(image, imgsz=1024, conf=0.25, iou=0.45, verbose=True, device=self.device)[0]
+        for xyxy, conf, cla in zip(doclayout_yolo_res.boxes.xyxy.cpu(), doclayout_yolo_res.boxes.conf.cpu(),
+                                   doclayout_yolo_res.boxes.cls.cpu()):
+            xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
+            new_item = {
+                'category_id': int(cla.item()),
+                'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
+                'score': round(float(conf.item()), 3),
+            }
+            layout_res.append(new_item)
+        return layout_res
\ No newline at end of file
--- a/magic_pdf/model/pek_sub_modules/structeqtable/__init__.py
+++ b/magic_pdf/model/pek_sub_modules/structeqtable/__init__.py
--- a/magic_pdf/model/v3/__init__.py
+++ b/magic_pdf/model/v3/__init__.py