neuralPlateRecognition
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.
SET UP:
Decompress the files in testing_full_system/ and training_ocr/ and the neural network library located in src/neural-networks-and-deep-learning.zip
GENERAL DESCRIPTION:
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
USAGE:
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/"