Merge pull request #2467 from opendatalab/release-1.3.11

Release 1.3.11

README.md

@@ -50,10 +50,8 @@ Easier to use: Just grab MinerU Desktop. No coding, no login, just a simple inte
 # Changelog
 - 2025/04/29 1.3.10 Released
   - Support for custom formula delimiters can be achieved by modifying the `latex-delimiter-config` item in the `magic-pdf.json` file under the user directory.
-  - Pinned `pdfminer.six` to version `20250324` to prevent parsing failures caused by new versions.
 - 2025/04/27 1.3.9 Released  
-  - Optimized the formula parsing function to improve the success rate of formula rendering  
-  - Updated `pdfminer.six` to the latest version, fixing some abnormal PDF parsing issues
+  - Optimized the formula parsing function to improve the success rate of formula rendering
 - 2025/04/23 1.3.8 Released
   - The default `ocr` model (`ch`) has been updated to `PP-OCRv4_server_rec_doc` (model update required)
     - `PP-OCRv4_server_rec_doc` is trained on a mix of more Chinese document data and PP-OCR training data, enhancing recognition capabilities for some traditional Chinese characters, Japanese, and special characters. It supports over 15,000 recognizable characters, improving text recognition in documents while also boosting general text recognition.
@@ -355,7 +353,7 @@ There are three different ways to experience MinerU:
     </tr>
     <tr>
         <td colspan="3">Python Version</td>
-        <td colspan="3">>=3.10</td>
+        <td colspan="3">3.10~3.13</td>
     </tr>
     <tr>
         <td colspan="3">Nvidia Driver Version</td>
@@ -365,8 +363,7 @@ There are three different ways to experience MinerU:
     </tr>
     <tr>
         <td colspan="3">CUDA Environment</td>
-        <td>11.8/12.4/12.6/12.8</td>
-        <td>11.8/12.4/12.6/12.8</td>
+        <td colspan="2"><a href="https://pytorch.org/get-started/locally/">Refer to the PyTorch official website</a></td>
         <td>None</td>
     </tr>
     <tr>
@@ -397,7 +394,7 @@ Synced with dev branch updates:
 #### 1. Install magic-pdf
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 pip install -U "magic-pdf[full]"
 ```

README_zh-CN.md

@@ -49,10 +49,8 @@
 # 更新记录
 - 2025/04/29 1.3.10 发布
   - 支持使用自定义公式标识符，可通过修改用户目录下的`magic-pdf.json`文件中的`latex-delimiter-config`项实现。
-  - 锁定`pdfminer.six`至`20250324`版本，以避免新版本导致的解析失败问题。
 - 2025/04/27 1.3.9 发布
   - 优化公式解析功能，提升公式渲染的成功率
-  - 更新`pdfminer.six`到最新版本，修复了部分pdf解析异常问题
 - 2025/04/23 1.3.8 发布
   - `ocr`默认模型(`ch`)更新为`PP-OCRv4_server_rec_doc`（需更新模型）
     - `PP-OCRv4_server_rec_doc`是在`PP-OCRv4_server_rec`的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力。
@@ -344,7 +342,7 @@ https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
     </tr>
     <tr>
         <td colspan="3">python版本</td>
-        <td colspan="3">>=3.10</td>
+        <td colspan="3">3.10~3.13</td>
     </tr>
     <tr>
         <td colspan="3">Nvidia Driver 版本</td>
@@ -354,8 +352,7 @@ https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
     </tr>
     <tr>
         <td colspan="3">CUDA环境</td>
-        <td>11.8/12.4/12.6/12.8</td>
-        <td>11.8/12.4/12.6/12.8</td>
+        <td colspan="2"><a href="https://pytorch.org/get-started/locally/">Refer to the PyTorch official website</a></td>
         <td>None</td>
     </tr>
     <tr>
@@ -390,7 +387,7 @@ https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
 > 最新版本国内镜像源同步可能会有延迟，请耐心等待
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 pip install -U "magic-pdf[full]" -i https://mirrors.aliyun.com/pypi/simple
 ```

docker/china/Dockerfile

@@ -45,7 +45,7 @@ RUN /bin/bash -c "wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/m
     pip3 install -U magic-pdf[full] -i https://mirrors.aliyun.com/pypi/simple"
 
 # Download models and update the configuration file
-RUN /bin/bash -c "pip3 install modelscope && \
+RUN /bin/bash -c "pip3 install modelscope -i https://mirrors.aliyun.com/pypi/simple && \
     wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/scripts/download_models.py -O download_models.py && \
     python3 download_models.py && \
     sed -i 's|cpu|cuda|g' /root/magic-pdf.json"

docs/README_Ubuntu_CUDA_Acceleration_en_US.md

@@ -54,7 +54,7 @@ In the final step, enter `yes`, close the terminal, and reopen it.
 ### 4. Create an Environment Using Conda
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 ```
 

docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md

@@ -54,7 +54,7 @@ bash Anaconda3-2024.06-1-Linux-x86_64.sh
 ## 4. 使用conda 创建环境
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 ```
 

docs/README_Windows_CUDA_Acceleration_en_US.md

@@ -2,11 +2,12 @@
 
 ### 1. Install CUDA and cuDNN
 
-You need to install a CUDA version that is compatible with torch's requirements. Currently, torch supports CUDA 11.8/12.4/12.6.
+You need to install a CUDA version that is compatible with torch's requirements. For details, please refer to the [official PyTorch website](https://pytorch.org/get-started/locally/).
 
 - CUDA 11.8 https://developer.nvidia.com/cuda-11-8-0-download-archive
 - CUDA 12.4 https://developer.nvidia.com/cuda-12-4-0-download-archive
 - CUDA 12.6 https://developer.nvidia.com/cuda-12-6-0-download-archive
+- CUDA 12.8 https://developer.nvidia.com/cuda-12-8-0-download-archive
 
 ### 2. Install Anaconda
 
@@ -17,7 +18,7 @@ Download link: https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Windows-x86
 ### 3. Create an Environment Using Conda
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 ```
 
@@ -63,7 +64,7 @@ If your graphics card has at least 6GB of VRAM, follow these steps to test CUDA-
 1. **Overwrite the installation of torch and torchvision** supporting CUDA.(Please select the appropriate index-url based on your CUDA version. For more details, refer to the [PyTorch official website](https://pytorch.org/get-started/locally/).)
 
    ```
-   pip install --force-reinstall torch torchvision "numpy<=2.1.1" --index-url https://download.pytorch.org/whl/cu124
+   pip install --force-reinstall torch torchvision --index-url https://download.pytorch.org/whl/cu124
    ```
 
 2. **Modify the value of `"device-mode"`** in the `magic-pdf.json` configuration file located in your user directory.

docs/README_Windows_CUDA_Acceleration_zh_CN.md

@@ -1,12 +1,13 @@
 # Windows10/11
 
-## 1. 安装cuda和cuDNN
+## 1. 安装cuda环境
 
-需要安装符合torch要求的cuda版本，torch目前支持11.8/12.4/12.6
+需要安装符合torch要求的cuda版本，具体可参考[torch官网](https://pytorch.org/get-started/locally/)
 
 - CUDA 11.8 https://developer.nvidia.com/cuda-11-8-0-download-archive
 - CUDA 12.4 https://developer.nvidia.com/cuda-12-4-0-download-archive
 - CUDA 12.6 https://developer.nvidia.com/cuda-12-6-0-download-archive
+- CUDA 12.8 https://developer.nvidia.com/cuda-12-8-0-download-archive
 
 ## 2. 安装anaconda
 
@@ -18,7 +19,7 @@ https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2024.06-1-Window
 ## 3. 使用conda 创建环境
 
 ```bash
-conda create -n mineru 'python>=3.10' -y
+conda create -n mineru 'python=3.12' -y
 conda activate mineru
 ```
 
@@ -64,7 +65,7 @@ pip install -U magic-pdf[full] -i https://mirrors.aliyun.com/pypi/simple
 **1.覆盖安装支持cuda的torch和torchvision**(请根据cuda版本选择合适的index-url，具体可参考[torch官网](https://pytorch.org/get-started/locally/))
 
 ```bash
-pip install --force-reinstall torch torchvision "numpy<=2.1.1" --index-url https://download.pytorch.org/whl/cu124
+pip install --force-reinstall torch torchvision --index-url https://download.pytorch.org/whl/cu124
 ```
 
 **2.修改【用户目录】中配置文件magic-pdf.json中"device-mode"的值**

magic_pdf/model/doc_analyze_by_custom_model.py

@@ -156,7 +156,10 @@ def doc_analyze(
         batch_images = [images_with_extra_info]
 
     results = []
-    for batch_image in batch_images:
+    processed_images_count = 0
+    for index, batch_image in enumerate(batch_images):
+        processed_images_count += len(batch_image)
+        logger.info(f'Batch {index + 1}/{len(batch_images)}: {processed_images_count} pages/{len(images_with_extra_info)} pages')
         result = may_batch_image_analyze(batch_image, ocr, show_log,layout_model, formula_enable, table_enable)
         results.extend(result)
 

magic_pdf/model/sub_modules/mfr/unimernet/unimernet_hf/modeling_unimernet.py

@@ -66,9 +66,9 @@ LEFT_RIGHT_REMOVE_PATTERN = re.compile(r'\\left\.?|\\right\.?')
 
 def fix_latex_left_right(s):
     """
-    修复LaTeX中的\left和\right命令
+    修复LaTeX中的\\left和\\right命令
     1. 确保它们后面跟有效分隔符
-    2. 平衡\left和\right的数量
+    2. 平衡\\left和\\right的数量
     """
     # 白名单分隔符
     valid_delims_list = [r'(', r')', r'[', r']', r'{', r'}', r'/', r'|',
@@ -106,7 +106,7 @@ def fix_latex_left_right(s):
 
 def fix_left_right_pairs(latex_formula):
     """
-    检测并修复LaTeX公式中\left和\right不在同一组的情况
+    检测并修复LaTeX公式中\\left和\\right不在同一组的情况
 
     Args:
         latex_formula (str): 输入的LaTeX公式
@@ -308,9 +308,9 @@ ENV_FORMAT_PATTERNS = {env: re.compile(r'\\begin\{' + env + r'\}\{([^}]*)\}') fo
 
 def fix_latex_environments(s):
     """
-    检测LaTeX中环境（如array）的\begin和\end是否匹配
-    1. 如果缺少\begin标签则在开头添加
-    2. 如果缺少\end标签则在末尾添加
+    检测LaTeX中环境（如array）的\\begin和\\end是否匹配
+    1. 如果缺少\\begin标签则在开头添加
+    2. 如果缺少\\end标签则在末尾添加
     """
     for env in ENV_TYPES:
         begin_count = len(ENV_BEGIN_PATTERNS[env].findall(s))
@@ -334,7 +334,7 @@ def fix_latex_environments(s):
 
 UP_PATTERN = re.compile(r'\\up([a-zA-Z]+)')
 COMMANDS_TO_REMOVE_PATTERN = re.compile(
-    r'\\(?:lefteqn|boldmath|ensuremath|centering|textsubscript|sides|textsl|textcent|emph)')
+    r'\\(?:lefteqn|boldmath|ensuremath|centering|textsubscript|sides|textsl|textcent|emph|protect|null)')
 REPLACEMENTS_PATTERNS = {
     re.compile(r'\\underbar'): r'\\underline',
     re.compile(r'\\Bar'): r'\\hat',
@@ -346,6 +346,9 @@ REPLACEMENTS_PATTERNS = {
     re.compile(r'\\textunderscore'): r'\\_',
     re.compile(r'\\fint'): r'⨏',
     re.compile(r'\\up '): r'\\ ',
+    re.compile(r'\\vline = '): r'\\models ',
+    re.compile(r'\\vDash '): r'\\models ',
+    re.compile(r'\\sq \\sqcup '): r'\\square ',
 }
 QQUAD_PATTERN = re.compile(r'\\qquad(?!\s)')
 

next_docs/en/user_guide/install/boost_with_cuda.rst

@@ -76,11 +76,11 @@ In the final step, enter ``yes``, close the terminal, and reopen it.
 4. Create an Environment Using Conda
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Specify Python version 3.10.
+Specify Python version 3.10～3.13.
 
 .. code:: sh
 
-    conda create -n mineru 'python>=3.10' -y
+    conda create -n mineru 'python=3.12' -y
     conda activate mineru
 
 5. Install Applications
@@ -155,14 +155,15 @@ to test CUDA acceleration:
 Windows 10/11
 --------------
 
-1. Install CUDA and cuDNN
+1. Install CUDA
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-You need to install a CUDA version that is compatible with torch's requirements. Currently, torch supports CUDA 11.8/12.4/12.6.
+You need to install a CUDA version that is compatible with torch's requirements. For details, please refer to the [official PyTorch website](https://pytorch.org/get-started/locally/).
 
 - CUDA 11.8 https://developer.nvidia.com/cuda-11-8-0-download-archive
 - CUDA 12.4 https://developer.nvidia.com/cuda-12-4-0-download-archive
 - CUDA 12.6 https://developer.nvidia.com/cuda-12-6-0-download-archive
+- CUDA 12.8 https://developer.nvidia.com/cuda-12-8-0-download-archive
 
 
 2. Install Anaconda
@@ -177,7 +178,7 @@ Download link: https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Windows-x86
 
 ::
 
-    conda create -n mineru 'python>=3.10' -y
+    conda create -n mineru 'python=3.12' -y
     conda activate mineru
 
 4. Install Applications

next_docs/en/user_guide/install/install.rst

@@ -61,7 +61,7 @@ Also you can try `online demo <https://www.modelscope.cn/studios/OpenDataLab/Min
     </tr>
     <tr>
         <td colspan="3">Python Version</td>
-        <td colspan="3">3.10~3.12</td>
+        <td colspan="3">3.10~3.13</td>
     </tr>
     <tr>
         <td colspan="3">Nvidia Driver Version</td>
@@ -71,8 +71,7 @@ Also you can try `online demo <https://www.modelscope.cn/studios/OpenDataLab/Min
     </tr>
     <tr>
         <td colspan="3">CUDA Environment</td>
-        <td>11.8/12.4/12.6/12.8</td>
-        <td>11.8/12.4/12.6/12.8</td>
+        <td colspan="2"><a href="https://pytorch.org/get-started/locally/">Refer to the PyTorch official website</a></td>
         <td>None</td>
     </tr>
     <tr>
@@ -97,7 +96,7 @@ Create an environment
 
 .. code-block:: shell
 
-    conda create -n mineru 'python>=3.10' -y
+    conda create -n mineru 'python=3.12' -y
     conda activate mineru
     pip install -U "magic-pdf[full]"
 

projects/multi_gpu/README.md

@@ -4,9 +4,7 @@
 ## 环境配置
 请使用以下命令配置所需的环境：
 ```bash
-pip install -U litserve python-multipart filetype
-pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
-pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118
+pip install -U magic-pdf[full] litserve python-multipart filetype
 ```
 
 ## 快速使用

projects/web_api/app.py

@@ -21,6 +21,7 @@ from magic_pdf.libs.config_reader import get_bucket_name, get_s3_config
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.operators.models import InferenceResult
 from magic_pdf.operators.pipes import PipeResult
+from fastapi import Form
 
 model_config.__use_inside_model__ = True
 
@@ -102,6 +103,7 @@ def init_writers(
         # 处理上传的文件
         file_bytes = file.file.read()
         file_extension = os.path.splitext(file.filename)[1]
+
         writer = FileBasedDataWriter(output_path)
         image_writer = FileBasedDataWriter(output_image_path)
         os.makedirs(output_image_path, exist_ok=True)
@@ -176,14 +178,14 @@ def encode_image(image_path: str) -> str:
 )
 async def file_parse(
     file: UploadFile = None,
-    file_path: str = None,
-    parse_method: str = "auto",
-    is_json_md_dump: bool = False,
-    output_dir: str = "output",
-    return_layout: bool = False,
-    return_info: bool = False,
-    return_content_list: bool = False,
-    return_images: bool = False,
+    file_path: str = Form(None),
+    parse_method: str = Form("auto"),
+    is_json_md_dump: bool = Form(False),
+    output_dir: str = Form("output"),
+    return_layout: bool = Form(False),
+    return_info: bool = Form(False),
+    return_content_list: bool = Form(False),
+    return_images: bool = Form(False),
 ):
     """
     Execute the process of converting PDF to JSON and MD, outputting MD and JSON files

requirements.txt

@@ -7,9 +7,9 @@ numpy>=1.21.6
 pydantic>=2.7.2,<2.11
 PyMuPDF>=1.24.9,<1.25.0
 scikit-learn>=1.0.2
-torch>=2.2.2,!=2.5.0,!=2.5.1
+torch>=2.2.2,!=2.5.0,!=2.5.1,<3
 torchvision
 transformers>=4.49.0,!=4.51.0,<5.0.0
-pdfminer.six==20250324
+pdfminer.six==20250506
 tqdm>=4.67.1
 # The requirements.txt must ensure that only necessary external dependencies are introduced. If there are new dependencies to add, please contact the project administrator.

setup.py

@@ -81,7 +81,7 @@ if __name__ == '__main__':
             "Programming Language :: Python :: 3.12",
             "Programming Language :: Python :: 3.13",
         ],
-        python_requires=">=3.10,<4",  # 项目依赖的 Python 版本
+        python_requires=">=3.10,<3.14",  # 项目依赖的 Python 版本
         entry_points={
             "console_scripts": [
                 "magic-pdf = magic_pdf.tools.cli:cli",

signatures/version1/cla.json

@@ -255,6 +255,14 @@
       "created_at": "2025-04-25T02:54:20Z",
       "repoId": 765083837,
       "pullRequestNo": 2367
+    },
+    {
+      "name": "CharlesKeeling65",
+      "id": 94165417,
+      "comment_id": 2841356871,
+      "created_at": "2025-04-30T09:25:31Z",
+      "repoId": 765083837,
+      "pullRequestNo": 2411
     }
   ]
 }