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E-ALPR

E-ALPR is an Automatic Licence Plate Recognition System for Egyptian Plates.

It uses the tiny version of the famous Yolo Model to predict the place of the plate then does some image processing to extract the characters from the plate then passes it to a generated Tensorflow model for recognizing the character using classification.

Requirements:

Make sure you have the necessary packages by doing this:

pip install -r requirements.txt

How to use:

First, you need to generate some characters data to train the recognition model on by running the Generate.py script with --fonts parameter taking the fonts directory that include the fonts you want to generate image for, and --out parameter referring to the output directory.

The default size of the generated images is 40x40 and 50 images per font, you can change it by passing the optional --size [SIZE] and --count [COUNT] parameters.

NOTE: the E-ALPR.py and E-ALPR_GUI.py scripts uses 40x40 image size only for now, you have to make adjustments to the code to make it compatible with the size you want.

Here's an example:

python Generate.py --fonts Fonts --out data

Please note that the generated data is synthetic data, that means you will not get 100% accurate results, you need to train on real data to get more accuracy.

After the data is generated, you need to train the recognition model. All you need to do is pass the --path parameter to Train.py script, like this:

python Train.py --path data

This will train a normal Tensorflow model for usage with the non-gui script, if you need to use the gui script you need to pass the --lite parameter to create a lite model.

python Train.py --path data --lite

Now, you can use either the E-ALPR.py script or E-ALPR_GUI.py script to predict and recognize the plate.

To use the non-gui version, you just need to pass the image to -i parameter and the created model to -m parameter.

python E-ALPR.py -i Test/test.jpg -m model.h5

If you want to predict on a video, you can use -v parameter or -c for a camera.

If you want to see what happens in the process, just add -d debugging parameter.

Special Thanks to:

Adrian Rosebrock and his amazing blog "PyImageSearch" that helped me a lot in making this project.

You can find the Blog here: PyImageSearch

Licence:

MIT