RILP

Robust Iranian License Plate Recognition Designed for Complex Conditions

This is a Modular Framework designed for License Plate Number Recognition in Complex Conditions. The distinct Design ables us to reconfigure the framework for other regions/Conditions in no time!

Prerequisites

The Following Setup is tested and working:

Python>=3.5
Pytorch>=0.4.1
Tensorflow>=1.12.2
Cuda>=9.0
opencv>=3.4.2

Testing

Place the images inside test_set/images directory
Delete all other images inside folders (don't delete the folders, just files inside them)
In main directory run: python3 runner.py

Training

The pre-trained model provided, we will not publish the training code
In order to train use pre-trained model or try another model

datasets

Glyphs

Properties:
- Volume: 5000 images
- Labeled
- Size: 100 x K (20 < K < 100)
Link:
- Will be avaiable soon, stay tuned.

Plates

Properties:
- Volume: 350 images
- Various conditions
- Various sizes
- License plate number (only one of them) is labeled
Link:
- Will be avaiable soon, stay tuned.

Citing

Please adequately refer to the papers any time this Work/Dataset is being used. If you do publish a paper where this Work helped your research, Please cite the following papers in your publications.

@inproceedings{Samadzadeh2020RILP,
  title={RILP: Robust Iranian License Plate Recognition Designed for Complex Conditions},
  author={Ali Samadzadeh, Amir Mehdi Shayan, Bahman Rouhani, Ahmad Nickabadi, Mohammad Rahmati},
  year={2020},
  organization={IEEE}}

9614priya/LP_recognition