Convolutional Recurrent Neural Network

This software implements the Convolutional Recurrent Neural Network (CRNN) in pytorch. Origin software could be found in crnn

Requirements

• pytorch 1.3+
• torchvision 0.4+

Data Preparation

Prepare a text in the following format

/path/to/img/img.jpg label
...

Performance

data link baiduyun code: 9p2m, the dataset is generate by https://github.com/Belval/TextRecognitionDataGenerator
dataset contains 10w images for train and 1w images for test:1w
for all arch ,we stop training after 30 epochs
environment: cuda9.2 torch1.4 torchvision0.5

arch	model size(m)	gpu mem(m)	speed(ms,avg of 100 inference)	acc
CNN_lite_LSTM_CTC	6.25	2731	6.91ms	0.8866
VGG(BasicConv)_LSTM_CTC(w320)	25.45	2409	4.02ms	0.9874
VGG(BasicConv)_LSTM_CTC(w160)	25.45	2409	4.02ms	0.9908
VGG(BasicConv)_LSTM_CTC(w160_no_imagenet_mean_std)	25.45	2409	4.02ms	0.9927
VGG(BasicConv)LSTM_CTC(w160.sub(0.5).div_(0.5))	25.45	2409	4.02ms	0.9927
VGG(BasicConv)_LSTM_CTC(w160 origin crnn rnn)	25.45	2409	4.02ms	0.9922
VGG(DWconv)_LSTM_CTC(w160_no_imagenet_mean_std)	25.45	2409	4.01ms	0.9725
VGG(GhostModule)_LSTM_CTC(w160_no_imagenet_mean_std)	25.45	2329	5.46ms	0.9878
ResNet(BasicBlockV2)_LSTM_CTC	37.21	3161	5.83ms	0.9935
ResNet(DWBlock_no_se)_LSTM_CTC	19.22	5533	12ms	0.9566
ResNet(DWBlock_se)_LSTM_CTC	19.90	5729	10ms	0.9559
ResNet(GhostBottleneck_se)_LSTM_CTC	23.10	6291	13ms	0.97

Train

1. config the dataset['train']['dataset']['data_path'],dataset['validate']['dataset']['data_path'] in config.yaml
2. generate alphabet use fellow script to generate alphabet.py in the some folder with train.py

python3 utils/get_keys.py

2. use following script to run

python3 train.py --config_path config.yaml

Predict

predict.py is used to inference on single image

1. config model_path, img_path in predict.py
2. use following script to predict

python3 predict.py