I realize three different models for text recognition, and all of them consist of CTC loss layer to realize no segmentation for text images.
I refer to the official mxnet warpctc example here.
- Build MXNet with Baidu Warp CTC, and please following this instructions here.
When I use this official instructions to add Baidu Warp CTC to Mxnet, there are some errors because the latest version of Baidu Warp CTC has complicts with mxnet. Recently, I see someone has already solved this problem and updated the official mxnet warpctc example. However, if you still have problem, please refer to this issue here.
Run generate_data.py in generate_data. When generating training and test data, please remember to change output path and number in generate_data.py (I will update a more friendly way to generate training and test data when I have free time).
I realize three different models for text recognition, you can check them in symbol:
- LSTM + CTC;
- Bidirection LSTM + CTC;
- CNN (a modified model similiar to VGG) + Bidirection LSTM + CTC. Disclaimer: This CNN + LSTM + CTC model is a re-implementation of original CRNN which is based on torch. The official repository is available here. The arxiv paper is available here.
- Start training:
LSTM + CTC:
python train_lstm.py
Bidirection LSTM + CTC:
python train_bi_lstm.py
CNN + Bidirection LSTM + CTC:
python train_crnn.py
You can do the prediction using your trained model. I only write the predictors for model 1 and model 3, but it is very easy to write the predictor for model 2 when referring to the examples.
Plesae run:
python lstm_predictor.py
or
python crnn_predictor.py
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