ocr pytorch attention-model Python
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This repository implements the the encoder and decoder model with attention model for OCR

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attention-ocr.pytorch:Encoder+Decoder+attention model

This repository implements the the encoder and decoder model with attention model for OCR, the encoder uses CNN+Bi-LSTM, the decoder uses GRU. This repository is modified from https://github.com/meijieru/crnn.pytorch
Earlier I had an open source version, but had some problems identifying images of fixed width. Recently I modified the model to support image recognition with variable width. The function is the same as CRNN. Due to the time problem, there is no pre-training model this time, which will be updated later.


pytorch 0.4.1
opencvpython ```bash cd Attentionocr.pytorch pip install -r requirements.txt ```


pretrained model coming soon


  1. Here i choose a small dataset from SyntheticChineseString_Dataset, about 270000+ images for training, 20000 images for testing. download the image data from Baidu
  2. the trainlist.txt and testlist.txt are created as the follow form:
    # path/to/image_name.jpg label
    path/AttentionData/50843500_2726670787.jpg 情笼罩在他们满是沧桑
    path/AttentionData/57724421_3902051606.jpg 心态的松弛决定了比赛
    path/AttentionData/52041437_3766953320.jpg 虾的鲜美自是不可待言
  3. change the trainlist and vallist parameter in train.py, and start train
    cd Attention_ocr.pytorch
    python train.py --trainlist ./data/ch_train.txt --vallist ./data/ch_test.txt
    then you can see in the terminel as follow: attentionocr there uses the decoderV2 model for decoder.

The previous version

git checkout AttentionOcrV1


  1. crnn.pytorch
  2. Attention-OCR
  3. Seq2Seq-PyTorch
  4. caffe_ocr


  • [ ] change LSTM to Conv1D, it can greatly accelerate the inference
  • [ ] change the cnn bone model with inception net, densenet
  • [ ] realize the decoder with transformer model

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