🚀 DeepSeek OCR - React + FastAPI

Modern OCR web application powered by DeepSeek-OCR with a stunning React frontend and FastAPI backend.

Note: This was a quickly vibe-coded project to test out DeepSeek-OCR! The initial version targeted an RTX 5090, but this fork refreshes the stack for AMD GPUs running ROCm 6.1. The "Find" mode grounding boxes aren't quite working yet - probably my fault in not interpreting the dimensions correctly, but the core OCR functionality is pretty nice so far.

Quick Start

docker compose up --build

Then open:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

Features

4 OCR Modes

• Plain OCR - Raw text extraction
• Describe - Generate image descriptions
• Find - Locate specific terms (grounding boxes WIP)
• Freeform - Custom prompts for anything

UI Features

• 🎨 Glass morphism design with animated gradients
• 🎯 Drag & drop file upload
• 📦 Grounding box visualization (WIP - dimensions need fixing)
• ✨ Smooth animations (Framer Motion)
• 📋 Copy/Download results
• 🎛️ Advanced settings dropdown
• 📝 Markdown rendering for formatted output

Tech Stack

• Frontend: React 18 + Vite 5 + TailwindCSS 3 + Framer Motion 11
• Backend: FastAPI + PyTorch + Transformers 4.46 + DeepSeek-OCR
• Server: Nginx (reverse proxy)
• Container: Docker + Docker Compose with multi-stage builds
• GPU: AMD ROCm 6.1 support (tested on Radeon PRO W7800)

Project Structure

deepseek-ocr/
├── backend/           # FastAPI backend
│   ├── main.py
│   ├── requirements.txt
│   └── Dockerfile
├── frontend/          # React frontend
│   ├── src/
│   │   ├── components/
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── package.json
│   ├── nginx.conf
│   └── Dockerfile
├── models/            # Model cache
└── docker-compose.yml

Development

Backend

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend

cd frontend
npm install
npm run dev

Requirements

• Docker & Docker Compose
• AMD GPU with ROCm 6.1 support (tested on Radeon PRO W7800)
• ROCm drivers/runtime on the host (/dev/kfd & /dev/dri exposed to containers)
• ~8-12GB VRAM for model

Known Issues

• 📦 Find mode grounding boxes: Not rendering correctly - likely dimension scaling issue in the canvas overlay logic. Boxes are detected and returned by the backend, but the frontend visualization needs work.

ROCm Notes

• The backend container is built from rocm/pytorch:rocm6.1_ubuntu22.04_py3.10_pytorch_2.5.1, so no extra PyTorch install steps are required.
• Ensure the host user has access to /dev/kfd and /dev/dri. Adding your user to the video and render groups usually does the trick.
• If your GPU reports an older GFX IP, set HSA_OVERRIDE_GFX_VERSION in .env or the compose file (for example 11.0.0 for RDNA3).
• Override TORCH_DEVICE or TORCH_DTYPE environment variables if you need to force a specific device/dtype (defaults are auto-detected).

API Usage

POST /api/ocr

Parameters:

• image (file, required)
• mode (string): plain_ocr | describe | find_ref | freeform
• prompt (string): Custom prompt for freeform mode
• grounding (bool): Enable bounding boxes (auto-enabled for find_ref)
• find_term (string): Term to locate in find_ref mode
• base_size (int): Base processing size (default: 1024)
• image_size (int): Image size (default: 640)
• crop_mode (bool): Enable crop mode (default: true)

Response:

{
  "success": true,
  "text": "Extracted text...",
  "boxes": [{"label": "field", "box": [x1, y1, x2, y2]}],
  "image_dims": {"w": 1920, "h": 1080},
  "metadata": {...}
}

Troubleshooting

GPU not detected

rocminfo
docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --group-add render rocm/pytorch:rocm6.1_ubuntu22.04_py3.10_pytorch_2.5.1 rocminfo

Port conflicts

sudo lsof -i :3000
sudo lsof -i :8000

Frontend build issues

cd frontend
rm -rf node_modules package-lock.json
docker-compose build frontend

License

This project uses the DeepSeek-OCR model. Refer to the model's license terms.