this code is based on the paper : LPRNet: License Plate Recognition via Deep Neural Networks https://arxiv.org/pdf/1806.10447.pdf
it is a kind of light network for plate recognition.
it uses CNN + CTC loss to recognise the plate without segmentation.
Step 1: set the default value
In the file LPRtf3.py：
num_epochs = 300
INITIAL_LEARNING_RATE = 1e-3
DECAY_STEPS = 2000
LEARNING_RATE_DECAY_FACTOR = 0.9 # The learning rate decay factor
MOMENTUM = 0.9
REPORT_STEPS = 5000
#the num of training data
BATCH_SIZE = 50
TRAIN_SIZE = 7368
BATCHES = TRAIN_SIZE//BATCH_SIZE
test_num = 3
ti = 'train' #the location of training data
vi = 'valid' #the location of validation data
img_size = [94, 24]
tl = None
vl = None
num_channels = 3
label_len = 7 #the length of plate character
Step 2: start training
running
$python3 LPRtf3.py
and then the screen will show:
'train or test:'
then:
input 'train' for training
input 'test' for testing
if you want to train your own data,you just need to rename your plate file as "province(convert the Chinese character to the coresponding code according to 'dict' in the LPRnet.py)_the numbers and alphabet of the plate", the examples is in the folder 'train'.