docs: update FAQ section in index.md with community support links and clarify...

docs: update FAQ section in index.md with community support links and clarify missing text issue in PDF rendering

docs/en/FAQ/index.md

 # Frequently Asked Questions
 
+If your question is not listed, you can also use [DeepWiki](https://deepwiki.com/opendatalab/MinerU) to communicate with the AI assistant, which can solve most common problems.
+
+If you still cannot resolve the issue, you can join the community through [Discord](https://discord.gg/Tdedn9GTXq) or [WeChat](http://mineru.space/s/V85Yl) to communicate with other users and developers.
+
 ## 1. Encountered the error `ImportError: libGL.so.1: cannot open shared object file: No such file or directory` in Ubuntu 22.04 on WSL2
 
 The `libgl` library is missing in Ubuntu 22.04 on WSL2. You can install the `libgl` library with the following command to resolve the issue:
@@ -21,3 +25,18 @@ pip install -U "mineru[pipeline_old_linux]"
 ```
 
 Reference: https://github.com/opendatalab/MinerU/issues/1004
+
+
+## 3. Missing text information in parsing results when installing and using on Linux systems.
+
+MinerU uses `pypdfium2` instead of `pymupdf` as the PDF page rendering engine in versions >=2.0 to resolve AGPLv3 license issues. On some Linux distributions, due to missing CJK fonts, some text may be lost during the process of rendering PDFs to images.
+To solve this problem, you can install the noto font package with the following commands, which are effective on Ubuntu/Debian systems:
+```bash
+sudo apt update
+sudo apt install fonts-noto-core
+sudo apt install fonts-noto-cjk
+fc-cache -fv
+```
+You can also directly use our [Docker deployment](../quick_start/docker_deployment.md) method to build the image, which includes the above font packages by default.
+
+Reference: https://github.com/opendatalab/MinerU/issues/2915

docs/en/known_issues.md

-# 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.
-- Limited support for vertical text.
-- Tables of contents and lists are recognized through rules, and some uncommon list formats may not be recognized.
-- 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.

docs/en/quick_start/local_deployment.md

-# Local Deployment
-
-## Install MinerU
-
-### Install via pip or uv
-
-```bash
-pip install --upgrade pip
-pip install uv
-uv pip install -U "mineru[core]"
-```
-
-### Install from source
-
-```bash
-git clone https://github.com/opendatalab/MinerU.git
-cd MinerU
-uv pip install -e .[core]
-```
-
-> [!NOTE]  
-> Linux and macOS systems automatically support CUDA/MPS acceleration after installation. For Windows users who want to use CUDA acceleration, 
-> please visit the [PyTorch official website](https://pytorch.org/get-started/locally/) to install PyTorch with the appropriate CUDA version.
-
-### Install Full Version (supports sglang acceleration) (requires device with Turing or newer architecture and at least 8GB GPU memory)
-
-If you need to use **sglang to accelerate VLM model inference**, you can choose any of the following methods to install the full version:
-
-- Install using uv or pip:
-  ```bash
-  uv pip install -U "mineru[all]"
-  ```
-- Install from source:
-  ```bash
-  uv pip install -e .[all]
-  ```
-
-> [!TIP]  
-> If any exceptions occur during the installation of `sglang`, please refer to the [official sglang documentation](https://docs.sglang.ai/start/install.html) for troubleshooting and solutions, or directly use Docker-based installation.
-
-- Build image using Dockerfile:
-  ```bash
-  wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/global/Dockerfile
-  docker build -t mineru-sglang:latest -f Dockerfile .
-  ```
-  Start Docker container:
-  ```bash
-  docker run --gpus all \
-    --shm-size 32g \
-    -p 30000:30000 \
-    --ipc=host \
-    mineru-sglang:latest \
-    mineru-sglang-server --host 0.0.0.0 --port 30000
-  ```
-  Or start using Docker Compose:
-  ```bash
-    wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
-    docker compose -f compose.yaml up -d
-  ```
-  
-> [!TIP]
-> The Dockerfile uses `lmsysorg/sglang:v0.4.8.post1-cu126` as the default base image, which supports the Turing/Ampere/Ada Lovelace/Hopper platforms.  
-> If you are using the newer Blackwell platform, please change the base image to `lmsysorg/sglang:v0.4.8.post1-cu128-b200`.
-
-### Install client  (for connecting to sglang-server on edge devices that require only CPU and network connectivity)
-
-```bash
-uv pip install -U mineru
-mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<host_ip>:<port>
-```
-
----
\ No newline at end of file

docs/en/quick_start/online_demo.md

-# Online Demo
-
-[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
-[![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)
\ No newline at end of file

docs/en/todo.md

-# TODO
-
-- [x] Reading order based on the model  
-- [x] Recognition of `index` and `list` in the main text  
-- [x] Table recognition
-- [x] Heading Classification
-- [ ] Code block recognition in the main text
-- [ ] [Chemical formula recognition](../chemical_knowledge_introduction/introduction.pdf)
-- [ ] Geometric shape recognition
\ No newline at end of file

docs/en/usage/api.md

-# API Calls or Visual Invocation
-
-1. Directly invoke using Python API: [Python Invocation Example](https://github.com/opendatalab/MinerU/blob/master/demo/demo.py)
-2. Invoke using FastAPI:
-   ```bash
-   mineru-api --host 127.0.0.1 --port 8000
-   ```
-   Visit http://127.0.0.1:8000/docs in your browser to view the API documentation.
-
-3. Use Gradio WebUI or Gradio API:
-   ```bash
-   # Using pipeline/vlm-transformers/vlm-sglang-client backend
-   mineru-gradio --server-name 127.0.0.1 --server-port 7860
-   # Or using vlm-sglang-engine/pipeline backend
-   mineru-gradio --server-name 127.0.0.1 --server-port 7860 --enable-sglang-engine true
-   ```
-   Access http://127.0.0.1:7860 in your browser to use the Gradio WebUI, or visit http://127.0.0.1:7860/?view=api to use the Gradio API.
-
-
-> [!TIP]  
-> - Below are some suggestions and notes for using the sglang acceleration mode:  
-> - The sglang acceleration mode currently supports operation on Turing architecture GPUs with a minimum of 8GB VRAM, but you may encounter VRAM shortages on GPUs with less than 24GB VRAM. You can optimize VRAM usage with the following parameters:  
->   - If running on a single GPU and encountering VRAM shortage, reduce the KV cache size by setting `--mem-fraction-static 0.5`. If VRAM issues persist, try lowering it further to `0.4` or below.  
->   - If you have more than one GPU, you can expand available VRAM using tensor parallelism (TP) mode: `--tp-size 2`  
-> - If you are already successfully using sglang to accelerate VLM inference but wish to further improve inference speed, consider the following parameters:  
->   - If using multiple GPUs, increase throughput using sglang's multi-GPU parallel mode: `--dp-size 2`  
->   - You can also enable `torch.compile` to accelerate inference speed by about 15%: `--enable-torch-compile`  
-> - For more information on using sglang parameters, please refer to the [sglang official documentation](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)  
-> - All sglang-supported parameters can be passed to MinerU via command-line arguments, including those used with the following commands: `mineru`, `mineru-sglang-server`, `mineru-gradio`, `mineru-api`
-
-> [!TIP]  
-> - In any case, you can specify visible GPU devices at the start of a command line by adding the `CUDA_VISIBLE_DEVICES` environment variable. For example:  
->   ```bash
->   CUDA_VISIBLE_DEVICES=1 mineru -p <input_path> -o <output_path>
->   ```
-> - This method works for all command-line calls, including `mineru`, `mineru-sglang-server`, `mineru-gradio`, and `mineru-api`, and applies to both `pipeline` and `vlm` backends.  
-> - Below are some common `CUDA_VISIBLE_DEVICES` settings:  
->   ```bash
->   CUDA_VISIBLE_DEVICES=1 Only device 1 will be seen
->   CUDA_VISIBLE_DEVICES=0,1 Devices 0 and 1 will be visible
->   CUDA_VISIBLE_DEVICES="0,1" Same as above, quotation marks are optional
->   CUDA_VISIBLE_DEVICES=0,2,3 Devices 0, 2, 3 will be visible; device 1 is masked
->   CUDA_VISIBLE_DEVICES="" No GPU will be visible
->   ```
-> - Below are some possible use cases:  
->   - If you have multiple GPUs and need to specify GPU 0 and GPU 1 to launch 'sglang-server' in multi-GPU mode, you can use the following command:  
->   ```bash
->   CUDA_VISIBLE_DEVICES=0,1 mineru-sglang-server --port 30000 --dp-size 2
->   ```
->   - If you have multiple GPUs and need to launch two `fastapi` services on GPU 0 and GPU 1 respectively, listening on different ports, you can use the following commands:  
->   ```bash
->   # In terminal 1
->   CUDA_VISIBLE_DEVICES=0 mineru-api --host 127.0.0.1 --port 8000
->   # In terminal 2
->   CUDA_VISIBLE_DEVICES=1 mineru-api --host 127.0.0.1 --port 8001
->   ```
-
----

docs/en/usage/config.md

-# Extending MinerU Functionality Through Configuration Files
-
-- MinerU is designed to work out-of-the-box, but also supports extending functionality through configuration files. You can create a `mineru.json` file in your home directory and add custom configurations.
-- The `mineru.json` file will be automatically generated when you use the built-in model download command `mineru-models-download`. Alternatively, you can create it by copying the [configuration template file](../../mineru.template.json) to your home directory and renaming it to `mineru.json`.
-- Below are some available configuration options:
-  - `latex-delimiter-config`: Used to configure LaTeX formula delimiters, defaults to the `$` symbol, and can be modified to other symbols or strings as needed.
-  - `llm-aided-config`: Used to configure related parameters for LLM-assisted heading level detection, compatible with all LLM models supporting the `OpenAI protocol`. It defaults to Alibaba Cloud Qwen's `qwen2.5-32b-instruct` model. You need to configure an API key yourself and set `enable` to `true` to activate this feature.
-  - `models-dir`: Used to specify local model storage directories. Please specify separate model directories for the `pipeline` and `vlm` backends. After specifying these directories, you can use local models by setting the environment variable `export MINERU_MODEL_SOURCE=local`.
-
----
\ No newline at end of file

mkdocs.yml

@@ -59,8 +59,6 @@ nav:
       - Output File Format: usage/output_file.md
   - FAQ:
       - FAQ: FAQ/index.md
-  - Known Issues: known_issues.md
-  - TODO: todo.md
 
 plugins:
   - search
@@ -85,8 +83,6 @@ plugins:
             Advanced CLI Parameters: 命令行参数进阶技巧
             FAQ: FAQ
             Output File Format: 输出文件格式
-            Known Issues: 已知问题
-            TODO: TODO
   - mkdocs-video
 
 markdown_extensions: