Quick Start

Requirements: Linux, NVIDIA driver 595+, Turing or newer GPU (RTX 20-series / GTX 16-series+). Plan for ~4 GB VRAM text-only and ~8 GB for the full pipeline (layout + tables + formulas); each extra PIPELINE_POOL_SIZE replica adds roughly another full set, so lower it on smaller cards.

docker run --gpus all -p 8000:8000 -p 50051:50051 \
  -v trt-cache:/home/ocr/.cache/turbo-ocr \
  ghcr.io/aiptimizer/turboocr:latest

First startup builds TensorRT engines from ONNX. This takes about 90 seconds on a 5090 GPU and up to an hour on older ones. Set TRT_OPT_LEVEL=3 to cut build time 3 to 5x with a small speed regression. The named volume caches the engines, so subsequent starts are instant. During the build, requests return a connection refused error from nginx until the backend is ready. nginx (port 8000) reverse-proxies to Drogon (port 8080), and both start automatically.

curl -X POST http://localhost:8000/ocr/raw \
  --data-binary @document.png -H "Content-Type: image/png"

{"results": [{"text": "Invoice Total", "confidence": 0.97, "bounding_box": [[42,10],[210,10],[210,38],[42,38]]}]}

Docker (recommended)

docker build -f docker/Dockerfile.gpu -t turboocr . docker run --gpus all -p 8000:8000 -p 50051:50051
-v trt-cache:/home/ocr/.cache/turbo-ocr turboocr

License

MIT. See LICENSE.

⭐ Star TurboOCR on GitHub
_{Sponsored by Miruiq — AI-powered data extraction from PDFs and documents — and DiaIQ.}