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Fast & Lightweight License Plate OCR

Intro

FastPlateOCR 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.

Features

  • Keras 3 Backend Support: Compatible with TensorFlow, JAX, and PyTorch backends 🧠
  • Augmentation Variety: Diverse 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 🌟

Model Zoo

We currently have the following available models:

Model Name Time b=1
(ms)[1]		 Throughput
(plates/second)[1]		 Dataset Accuracy[2] Dataset
argentinian-plates-cnn-model 2.1 476 arg_plate_dataset.zip 94.05% Non-synthetic, plates up to 2020.
argentinian-plates-cnn-synth-model 2.1 476 arg_plate_dataset.zip 94.19% Plates up to 2020 + synthetic plates.
🆕 european-plates-mobile-vit-v2-model 2.9 344 - 92.5%[3] European plates (+40 countries).

[1] Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed by 5x in the CNN models.

[2] Accuracy is what we refer as plate_acc. See metrics section.

[3] For detailed accuracy for each country see results and the corresponding val split used.

Reproduce results. Calculate Inference Time: 
pip install fast_plate_ocr

from fast_plate_ocr import ONNXPlateRecognizer

m = ONNXPlateRecognizer("argentinian-plates-cnn-model")
m.benchmark()
Calculate Model accuracy: 
pip install fast-plate-ocr[train]
curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr_config.yaml
curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_cnn_ocr.keras
curl -LO https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_benchmark.zip
unzip arg_plate_benchmark.zip
fast_plate_ocr valid \
    -m arg_cnn_ocr.keras \
    --config-file arg_cnn_ocr_config.yaml \
    --annotations benchmark/annotations.csv