KR_LPR_TF
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A lightweight real-time model for South Korean license plate recognition.
Korean License Plate Recognition
This repository is no longer being updated. For a more readable version of License Plate Recognition using Jax, please refer here.
This repository is based on Self-supervised Implicit Glyph Attention for Text Recognition. It is a Tensorflow implementation of Korean license plate recognition.
Demo
Requirements
- Python 3.11.6
- Tensorflow 2.15.0
- numba
- numpy
- opencv-python
- Pillow
Dataset
- Real Korean License Plate Dataset
- Synthetic Korean License Plate Dataset
Training
python train.py
Evaluation
python model/eval.py
Model Architecture
Model Performance
The best model saved in ./checkpoints/backup/best.keras
.
task | accuracy |
---|---|
LPR w/ RE | 100.00 % |
LPR w/o RE | 99.15 % |
Character Recognition | 99.89 % |
* All accuracy is calculated on unquantized model
Computational Cost
task | parameters | FLOPs | size |
---|---|---|---|
LPR deployment | 32,207 | 9.58 M | 83 KB |
Speed of Inference
task | platform | quantization | time |
---|---|---|---|
LPR w/o RE | Apple M2 | uint8 | 0.14 ms |
LPR w/o RE | Intel i9-10900K | uint8 | - ms |
LPR w/o RE | AMD EPYC 7V12 | uint8 | 0.42 ms |
LPR w/o RE | Coral Edge TPU | uint8 | - ms |
One More Thing
The jax implementation of this repository is available at here.