⚡ Project Overview

This project is a multimodal document parsing tool based on DeepSeek-OCR with React frontend and FastAPI backend. This tool can efficiently process PDF documents and images, providing powerful Optical Character Recognition (OCR) capabilities, supporting multi-language text recognition, table parsing, chart analysis, and many other features.

Key Features

• Multi-format Document Parsing: Supports uploading and parsing documents in various formats such as PDF and images
• Intelligent OCR Recognition: Based on the DeepSeek-OCR model, providing high-precision text recognition
• Layout Analysis: Intelligently recognizes document layout structure and accurately extracts content layout
• Multi-language Support: Supports text recognition in multiple languages including Chinese and English
• Table & Chart Parsing: Professional table recognition and chart data extraction functionality
• Professional Domain Drawing Recognition: Supports semantic recognition of various professional domain drawings
• Data Visualization: Supports reverse parsing of data analysis visualization charts
• Markdown Conversion: Converts PDF content to structured Markdown format

👀 Project Demo

🚀 Usage Guide

System Requirements

⚠️ Important Notice:

• Operating System: Requires running on Linux system
• GPU Requirements: GPU ≥ 7 GB VRAM (16–24 GB recommended for large images/multi-page PDFs)
• Compatibility Note: RTX 50 series GPUs are currently not compatible, please use other GPU models
• Python Version: 3.10–3.12 (3.10/3.11 recommended)
• CUDA Version: 11.8 or 12.1/12.2 (must match GPU driver)
• PyTorch: Requires installing pre-compiled version matching CUDA

Quick Start

Method 1: One-click Script Startup (Recommended)

Execute the following script for one-click startup

# Install model weights and environment dependencies
bash install.sh
# Start services
bash start.sh

Method 2: Manual Installation and Running

Step 1: Model Weight Download

First, you need to download the DeepSeek-OCR model weights, which can be obtained from Hugging Face or ModelScope. The following example uses ModelScope:

pip install modelscope
mkdir ./deepseek-ocr
modelscope download --model deepseek-ai/DeepSeek-OCR --local_dir ./deepseek-ocr

Step 2: Runtime Environment Setup

Download the official project package

git clone https://github.com/deepseek-ai/DeepSeek-OCR.git

Create a virtual environment to install model runtime dependencies

conda create -n deepseek-ocr python=3.12.9 -y
conda activate deepseek-ocr

Install Jupyter and corresponding kernel

conda install jupyterlab
conda install ipykernel
python -m ipykernel install --user --name dsocr --display-name "Python (dsocr)"

Install PyTorch related components

pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118

Install DeepSeek-OCR officially recommended vLLM version (v0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl)

pip install ./packages/vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl

Install project basic dependencies

cd ./DeepSeek-OCR/
pip install -r requirements.txt

If dependency conflicts appear during installation as shown in the image, you can ignore them as they won't affect actual operation.

Install flash-attn acceleration library.

pip install flash-attn==2.7.3 --no-build-isolation

Create a .env file in the project root directory and enter the model runtime address, for example:

MODEL_PATH=/root/autodl-tmp/deepseek-ocr

Step 3: Start Backend Service

Start the backend

uvicorn main:app --host 0.0.0.0 --port 8002 --reload

Step 4: Start Frontend Service

Install frontend dependencies

npm install

Start the frontend

npm run dev

After successful startup, access the frontend address in your browser to use the tool.

🙈 Contributing

We welcome contributions to the project through GitHub PR submissions or issues. We very much welcome any form of contribution, including feature improvements, bug fixes, or documentation optimization.

😎 Technical Communication

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