CRNN_Chinese_Characters_Rec

Characters Recognition

A Chinese characters recognition repository based on convolutional recurrent networks.

## Performance #### Recognize characters in pictures

Dev Environments

1. WIN 10 or Ubuntu 16.04
2. PyTorch 1.2.0 (may fix ctc loss) with cuda 10.0 🔥
3. yaml
4. easydict
5. tensorboardX

Data

Synthetic Chinese String Dataset

1. Download the dataset

2. Edit lib/config/360CC_config.yaml DATA:ROOT to you image path

   DATASET:
     ROOT: 'to/your/images/path'
   

3. Download the labels (password: eaqb)

4. Put char_std_5990.txt in lib/dataset/txt/

5. And put train.txt and test.txt in lib/dataset/txt/

   eg. test.txt

   20456343_4045240981.jpg 89 201 241 178 19 94 19 22 26 656
   20457281_3395886438.jpg 120 1061 2 376 78 249 272 272 120 1061
   ...
   

   Or your own data

6. Edit lib/config/OWN_config.yaml DATA:ROOT to you image path

   DATASET:
     ROOT: 'to/your/images/path'
   

7. And put your train_own.txt and test_own.txt in lib/dataset/txt/

   eg. test_own.txt

   20456343_4045240981.jpg 你好啊！祖国！
   20457281_3395886438.jpg 晚安啊！世界！
   ...
   

   note: fixed-length training is supported. yet you can modify dataloader to support random length training.

Train

   [run] python train.py --cfg lib/config/360CC_config.yaml
or [run] python train.py --cfg lib/config/OWN_config.yaml

#### loss curve

angular2html [run] cd output/360CC/crnn/xxxx-xx-xx-xx-xx/ [run] tensorboard --logdir log

#### loss overview(first epoch)
<center/>
<img src='images/train_loss.png' title='loss1' style='max-width:800px'></img>
</center>
<p>
<img src='images/tb_loss.png' title='loss1' style='max-width:600px'></img>
</p>

## Demo

angular2html [run] python demo.py --image_path images/test.png --checkpoints output/checkpoints/mixed_second_finetune_acc_97P7.pth ```

References

• https://github.com/meijieru/crnn.pytorch
• https://github.com/HRNet