Convolutional Recurrent Neural Network

This software implements OCR system using CNN + RNN + CTCLoss, inspired by CRNN network.

Usage

python ./train.py --help

Demo

1. Train simple OCR using TestDataset data generator. Training for ~60-100 epochs.

python train.py --test-init True --test-epoch 10 --output-dir <path_to_folder_with_snapshots>

2. Run test for trained model with visualization mode.

python test.py --snapshot <path_to_folder_with_snapshots>/crnn_resnet18_10_best --visualize True

Train on custom dataset

1. Create dataset

• Structure of dataset:

<root_dataset_dir>
---- data
-------- <img_filename_0>
...
-------- <img_filename_1>
---- desc.json

• Structure of desc.json:

{
"abc": <symbols_in_aphabet>,
"train": [
{
"text": <text_on_image>
"name": <img_filename>
},
...
{
"text": <text_on_image>
"name": <img_filename>
}
],
"test": [
{
"text": <text_on_image>
"name": <img_filename>
},
...
{
"text": <text_on_image>
"name": <img_filename>
}
]
}

2. Train simple OCR using custom dataset.

python train.pt --test-init True --test-epoch 10 --output-dir <path_to_folder_with_snapshots> --data-path <path_to_custom_dataset>

3. Run test for trained model with visualization mode.

python test.py --snapshot <path_to_folder_with_snapshots>/crnn_resnet18_10_best --visualize True --data-path <path_to_custom_dataset>

Dependence

• pytorch 0.3.0 +
• warp-ctc

Articles

• An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks