PaddleOCR
Provides OCR capabilities using PaddleOCR (based on PaddlePaddle), allowing AI agents to extract text from images locally.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PaddleOCRExtract text from /home/user/receipt.jpg and list the items."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
PaddleOCR MCP Server
🏮 中文
PaddleOCR MCP Server
基于 PaddleOCR 的 模型上下文协议 (MCP) 服务器,为 AI Agent 提供本地图片文字识别能力。
🚀 核心优势
特性 | PaddleOCR MCP | 云端 OCR API |
费用 | 💰 免费,零成本 | 按次收费 |
隐私 | 🔒 100% 本地,数据不出设备 | 数据上传云端 |
网络 | 📡 离线可用,无需联网 | 依赖网络 |
延迟 | ⚡ ~1-3 秒 | ~0.5-3 秒 + 网络耗时 |
语言 | 🌍 支持 109 种语言 | 视服务商而定 |
模型 | 🤖 支持 DeepSeek-v4 等任意大模型搭配 | 厂商锁定 |
开源 | 📂 完全开源,可自由修改 | 闭源黑盒 |
部署 | 🖥️ 低成本 CPU 即可运行 | 需高成本服务器 |
💡 搭配 DeepSeek-v4 的低成本方案:PaddleOCR 负责提取图片文字,DeepSeek-v4 等大模型负责理解分析——本地运行,无需 GPU,开发成本趋近于零。
Related MCP server: Mistral OCR MCP Server
🤖 自动识别机制
即使你的模型不支持看图(纯文本模型),AI Agent 也能自动调用
ocr_imageMCP 工具识别图片文字后返回结果。
举个例子:
用户发送一张截图
AI Agent 发现该模型不支持直接看图
自动调用
ocr_image工具提取图片中的文字基于提取的文字内容进行分析和回答
这意味着:
✅ 无需切换模型 — 纯文本模型也能"看懂"图片
✅ 零额外配置 — MCP 挂载后自动生效
✅ 透明使用 — 用户只需发图,Agent 自动处理
✨ 功能特性
纯文字识别 —
ocr_image(image_path)一键提取图片中全部文字中英混合 — 原生支持中文 + 英文混合识别
109 种语言 — 覆盖全球主要语言
本地离线 — 无需 API Key,首次下载模型后完全离线
轻量快速 — PP-OCRv6 模型仅 34.5M 参数,CPU 流畅运行
MCP 标准 — 兼容所有 MCP 客户端
🎯 适用场景
🤖 搭配 DeepSeek-v4、GLM、Qwen 等大模型做图片内容理解
📄 文档扫描件、截图文字提取
🏭 工业场景:车牌、标签、单据识别
🌐 多语言文档处理
📦 快速开始
环境要求
Python 3.8~3.12
Node.js 18+(仅 Claude Code 等客户端需要,服务端本身不需要)
安装
# 安装 PaddleOCR 和 PaddlePaddle
pip install paddlepaddle==3.2.0 paddleocr
# 克隆本仓库
git clone https://github.com/YuanJinke/paddleocr_mcp.git
cd paddleocr_mcp⚠️ Windows 用户注意:PaddlePaddle 3.3.1 有 oneDNN 兼容性问题,请务必安装 3.2.0 版本。
配置到你的 MCP 客户端
claude mcp add -s user paddleocr -- python /路径/paddleocr_mcp.py或添加到 ~/.claude.json:
{
"mcpServers": {
"paddleocr": {
"type": "stdio",
"command": "python",
"args": ["/路径/paddleocr_mcp.py"]
}
}
}添加到 ~/.hermes/config.yaml:
mcp_servers:
paddleocr:
command: python
args: ["/路径/paddleocr_mcp.py"]
timeout: 120
connect_timeout: 120然后重启 Hermes。
{
"mcpServers": {
"paddleocr": {
"type": "stdio",
"command": "python",
"args": ["/路径/paddleocr_mcp.py"]
}
}
}使用示例
向你的 AI Agent 发送:
调用
ocr_image识别/图片路径/screenshot.png,然后告诉我图片里写了什么。
返回结果示例:
{"text": "Hello World\n你好世界", "lines": ["Hello World", "你好世界"]}🔧 本地测试
# 启动服务
python paddleocr_mcp.py
# 另开终端发送测试请求
echo '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"ocr_image","arguments":{"image_path":"/路径/test.png"}},"id":1}' | python paddleocr_mcp.py⚠️ 已知问题
Windows + oneDNN:PaddlePaddle 3.3.1 有 oneDNN 属性转换 bug。使用 3.2.0 或设置
FLAGS_use_onednn=0首次运行:自动下载模型权重(约 50 MB),后续使用缓存
📄 许可证
MIT — 随意使用,随意修改。
🌐 English
PaddleOCR MCP Server
A Model Context Protocol (MCP) server providing local OCR capabilities via PaddleOCR. Works with any MCP-compatible AI agent.
🚀 Key Advantages
Feature | PaddleOCR MCP | Cloud OCR APIs |
Cost | 💰 Free | Pay per request |
Privacy | 🔒 100% local, data never leaves | Data sent to cloud |
Network | 📡 Offline, no internet needed | Internet required |
Latency | ⚡ ~1-3s | ~0.5-3s + network |
Languages | 🌍 109 languages supported | Varies |
Model | 🤖 Works with DeepSeek-v4, any LLM | Vendor locked |
Open Source | 📂 Fully open source | Closed source |
Deployment | 🖥️ Runs on CPU, low cost | Requires servers |
💡 Low-cost solution with DeepSeek-v4: PaddleOCR extracts text from images, then pairs with any LLM (DeepSeek-v4, GPT, Claude, GLM, etc.) for understanding — all running locally, zero GPU required, near-zero development cost.
🤖 Auto-OCR Mechanism
Even if your model doesn't support images (text-only model), the AI Agent will automatically call the
ocr_imageMCP tool to read the image and return the text content.
How it works:
User sends a screenshot
AI Agent detects the model can't process images natively
Auto-calls
ocr_imageto extract text from the imageAnalyzes and responds based on the extracted text
This means:
✅ No model switching required — text-only models can "see" images
✅ Zero extra config — works automatically once MCP is mounted
✅ Transparent usage — just send the image, the agent handles the rest
✨ Features
Pure OCR —
ocr_image(image_path)extracts all text from imagesChinese + English — mixed-language recognition out of the box
109 languages — global language coverage
Local & offline — no API keys, no network calls after initial model download
Lightweight — PP-OCRv6 (34.5M params) runs on CPU
MCP standard — compatible with all MCP clients
🎯 Use Cases
🤖 DeepSeek-v4, GLM, Qwen image understanding pipeline
📄 Document scanning & screenshot OCR
🏭 Industrial: license plates, labels, receipts
🌐 Multilingual document processing
📦 Quick Start
Prerequisites
Python 3.8–3.12
Node.js 18+ (for client-side tools only, not needed by the server itself)
Install
# Install PaddleOCR and PaddlePaddle
pip install paddlepaddle==3.2.0 paddleocr
# Clone this repo
git clone https://github.com/YuanJinke/paddleocr_mcp.git
cd paddleocr_mcp⚠️ Windows users: PaddlePaddle 3.3.1 has a known oneDNN bug. Pin to 3.2.0 as shown above.
Configure in your MCP client
claude mcp add -s user paddleocr -- python /path/to/paddleocr_mcp.pyOr add to ~/.claude.json:
{
"mcpServers": {
"paddleocr": {
"type": "stdio",
"command": "python",
"args": ["/path/to/paddleocr_mcp.py"]
}
}
}Add to ~/.hermes/config.yaml:
mcp_servers:
paddleocr:
command: python
args: ["/path/to/paddleocr_mcp.py"]
timeout: 120
connect_timeout: 120Then restart Hermes.
{
"mcpServers": {
"paddleocr": {
"type": "stdio",
"command": "python",
"args": ["/path/to/paddleocr_mcp.py"]
}
}
}Usage Example
Send to your AI agent:
Call
ocr_imageon `/path/to/screenshot.png" and tell me what it says.
Example result:
{"text": "Hello World\n你好世界", "lines": ["Hello World", "你好世界"]}🔧 Local Test
# Start server
python paddleocr_mcp.py
# Send test request
echo '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"ocr_image","arguments":{"image_path":"/path/to/test.png"}},"id":1}' | python paddleocr_mcp.py⚠️ Known Issues
Windows + oneDNN: PaddlePaddle 3.3.1 has a oneDNN attribute conversion bug. Use 3.2.0 or set
FLAGS_use_onednn=0.First run: Model weights (~50 MB) auto-downloaded on first use. Subsequent runs use cache.
📄 License
MIT — free to use, modify, and distribute.
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