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

A demo program can be found in demo.py. Before running the demo, download a pretrained model from Baidu Netdisk or Dropbox. This pretrained model is converted from auther offered one by tool. Put the downloaded model file crnn.pth into directory data/. Then launch the demo by:

python demo.py

The demo reads an example image and recognizes its text content.

Example image:

Expected output: loading pretrained model from ./data/crnn.pth a-----v--a-i-l-a-bb-l-ee-- => available

• warp_ctc_pytorch
• lmdb

1. Construct dataset following origin guide. If you want to train with variable length images (keep the origin ratio for example), please modify the tool/create_dataset.py and sort the image according to the text length.
2. Execute python train.py --adadelta --trainRoot {train_path} --valRoot {val_path} --cuda. Explore train.py for details.

@article{shi2016end,
  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={39},
  number={11},
  pages={2298--2304},
  year={2016},
  publisher={IEEE}
}