This project is developed in Python using OpenCV and the free deep learning library neural-networks-and-deep-learning. It is capable of recognizing cars and reading the plate number. It recognizes 37 different values: all characters from A-Z, digits from 0-9 and the european symbol of Spain in the plate.

Decompress the files in testing_full_system/ and training_ocr/ and the neural network library located in src/neural-networks-and-deep-learning.zip

You can test the system executing processer.py (See USAGE). The default neural net used is general_v2 which is the one that gave the best results. If you want to train a new neural network, you only have to execute neural.py and follow the steps. You can train it for a few epochs and then change parameters and keep training. You can even load an existing net and train keep training it if you wish.

The network general_v2 was trained with 100 input neurons, 100 first and 100 second level neurons and 37 output neurons. Used parameters: Epochs: 100 Batch-Size: 10 Learning rate: 0.1 Lambda: 5.0

From src/:

• Test a real-world scenario: python main.py "../testing_full_system/testing_full_system"
• Train a new neural network (new or existing): python neural.py "../training_ocr/training_ocr/" "../training_ocr/testing_ocr/"