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Fast & Lightweight License Plate OCR

Intro

fast-plate-ocr is a lightweight and fast OCR framework for license plate text recognition. You can train models from scratch or use the trained models for inference.

The idea is to use this after a plate object detector, since the OCR expects the cropped plates.

🚀 Try it on Hugging Face Spaces!

You can try fast-plate-ocr pre-trained models in Hugging Spaces. No setup required!

Features

  • Keras 3 Backend Support: Train seamlessly using TensorFlow, JAX, or PyTorch backends 🧠
  • Augmentation Variety: Diverse training-time augmentations via Albumentations library 🖼️
  • Efficient Execution: Lightweight models that are cheap to run 💰
  • ONNX Runtime Inference: Fast and optimized inference with ONNX runtime ⚡
  • User-Friendly CLI: Simplified CLI for training and validating OCR models 🛠️
  • Model HUB: Access to a collection of pre-trained models ready for inference 🌟
  • Train/Fine-tune: Easily train or fine-tune your own models 🔧
  • Export-Friendly: Export easily to CoreML or TFLite formats 📦

Quick Installation

Install for inference:

pip install fast-plate-ocr[onnx-gpu]

Install for training:

pip install fast-plate-ocr[train]

For full installation options (like GPU backends or ONNX variants), see the Installation Guide.

Quick Usage

Run OCR on a cropped license plate image using LicensePlateRecognizer:

from fast_plate_ocr import LicensePlateRecognizer

m = LicensePlateRecognizer("cct-xs-v1-global-model")
print(m.run("test_plate.png"))

For more examples and input formats (NumPy arrays, batches, etc.), see the Inference Guide.

Use it with FastALPR

If you prefer not to use fast-plate-ocr directly on cropped plates, you can easily leverage it through FastALPR, an end-to-end Automatic License Plate Recognition library where fast-plate-ocr serves as the default OCR backend.

from fast_alpr import ALPR  # (1)!

alpr = ALPR(
    detector_model="yolo-v9-t-384-license-plate-end2end",
    ocr_model="global-plates-mobile-vit-v2-model",  # (2)!
)

alpr_results = alpr.predict("assets/test_image.png")
print(alpr_results)
  1. Requires fast-alpr package to be installed!
  2. Can be any of the default fast-plate-ocr trained models or custom ones too!

Explore More

Check out the FastALPR docs for full ALPR pipeline and integration tips!