/My_Work_Digital_Recongnition

Documenting my graduate research journey on the subject of identifying the readings of electronic scales

Primary LanguagePythonMIT LicenseMIT

My graduate work

Documenting my graduate research journey on the subject of electronic scale readings for s

requirements

pytorch 1.11.0 python 3.8.13

Test

pretrained model coming soon

Train

  1. Here i choose a small dataset from Synthetic_Chinese_String_Dataset, about 270000+ images for training, 20000 images for testing. download the image data from Baidu
  2. the train_list.txt and test_list.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
  1. 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

Reference

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

TO DO

  • 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