Convolutional Recurrent Neural Network

This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR. For details, please refer to our paper http://arxiv.org/abs/1507.05717.

Build

The software has only been tested on Ubuntu 14.04 (x64). CUDA-enabled GPUs are required. To build the project, first install Torch7, TH++ and LMDB. Please follow their installation instructions. On Ubuntu, lmdb can be installed by apt-get install liblmdb-dev.

To build the project, go to src/ and execute sh build_cpp.sh to build the C++ code. If successful, a file named libcrnn.so should be produced in the src/ directory.

Run demo

A demo program can be found in src/demo.lua. Before running the demo, download a pretrained model from here. Put the downloaded model file crnn_demo_model.t7 into directory model/crnn_demo/. Then launch the demo by:

th demo.lua

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

Example image:

Expected output:

Loading model...
Model loaded from ../model/crnn_demo/model.t7
Recognized text: available (raw: a-----v--a-i-l-a-bb-l-e---)

Use pretrained model

The pretrained model can be used for lexicon-free and lexicon-based recognition tasks. Refer to the functions recognizeImageLexiconFree and recognizeImageWithLexicion in file utilities.lua for details.

Train a new model

Follow the following steps to train a new model on your own dataset.

Create a new LMDB dataset. A python program is provided in tool/create_dataset.py. Refer to the function createDataset for details.
Create model directory under model/. For example, model/foo_model. Then create configuraton file config.lua under the model directory. You can copy model/crnn_demo/config.lua and do modifications.
Go to src/ and execute th main_train.lua ../models/foo_model/. Model snapshots and logging file will be saved into the model directory.

Citation

Please cite the following paper if you are using the code/model in your research paper.

@article{ShiBY15,
  author    = {Baoguang Shi and
               Xiang Bai and
               Cong Yao},
  title     = {An End-to-End Trainable Neural Network for Image-based Sequence Recognition
               and Its Application to Scene Text Recognition},
  journal   = {CoRR},
  volume    = {abs/1507.05717},
  year      = {2015}
}

Acknowledgements

The authors would like to thank the developers of Torch7, TH++, lmdb-lua-ffi and char-rnn.

Please let me know if you encounter any issues.

realzheng / crnn