Japanese License Plate Recognition

Japanese license plate recognition project implemented with PyTorch, YOLOv8 and OpenCV. For research purpose only.

Gradio App

Hugging Face Spaces

Check out the Gradio app on Hugging Face Spaces https://huggingface.co/spaces/eepj/lprs-jp.

Japanese License Plate Format

Markings

① Name of Region Registered
② Classification Number
③ Kana Character
④ 4-Digit Designation Number (Leading zeros are shown as ．)

Color Scheme

License Plate Type	Engine Displacement	Marking Color	Background Color
Private	≥ 660 cc	Green	White
Private	< 660 cc	Black	Yellow
Commercial	≥ 660 cc	White	Green
Commercial	< 660 cc	Yellow	Black
Commemorative	–	Green	Multiple
Glowing	–	Neon Green	White

Datasets

License Plates Dataset

• Dataset comprising 350 vehicles and their corresponding license plate bounding boxes for fine-tuning YOLOv8 segmentation model to detect license plates from images.

alpr_jp

• Dataset comprising 1000+ unlabeled Japanese license plate images for training character recognition models.
• Google Search images were used to supplement the dataset in case of missing or less common markings.
• All markings were manually labeled.

Approach

Training

Model

• CNN adapted from Chinese License Plate Recognition System Based on Convolutional Neural Network, layer depths adjusted according to specific recognition task.

Hardware

• Apple M1 with MPS hardware acceleration

Hyperparameters

• Number of epochs: 100
• Optimizer: Adam
• Initial learning rate: 1e-3
• Learning rate scheduler: StepLR, reduce by factor of 0.1 every 30 epochs
• Loss function: CrossEntropyLoss
• Random seed: 42

Data Augmentation

• Training images were passed to a 7-step augmenetation pipeline to enhance the model's robustness against image quality, camera angles and color variations.

Performance

Metrics

Recognition Task	Convolutional Layer Depths	Samples (Classes)	Accuracy	Weighted F1	Params (×103)
① Region Name	64, 128, 256, 512	412 (134)	0.97573	0.97265	1690
② Classification Number	64, 128, 256	444 (11)	0.98423	0.98426	440
③ Kana Character	64, 128, 256, 512	430 (43)	0.97907	0.97837	680
④ Designation Number	64, 128, 256, 512	547 (11)	0.99817	0.99817	646

Example Test Case

References

alpr_jp
Big thanks to dyama san for sharing the alpr_jp dataset.
https://github.com/dyama/alpr_jp

License Plates Dataset
https://universe.roboflow.com/samrat-sahoo/license-plates-f8vsn

YOLOv8
https://github.com/ultralytics/ultralytics

Chinese License Plate Recognition System Based on Convolutional Neural Network
H. Chen, Y. Lin, and T. Zhao, 'Chinese License Plate Recognition System Based on Convolutional Neural Network', Highlights in Science, Engineering and Technology, vol. 34, pp. 95–102, 2023.
https://www.researchgate.net/publication/369470024

ナンバープレートの見方 (How to Read a Number Plate)
https://wwwtb.mlit.go.jp/tohoku/jg/jg-sub29_1.html

Fun Fact

This repository was created on Leap Day 2024.