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# Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach

Paper: https://arxiv.org/abs/2308.08806

This an implementation based on PyTorch only. In the paper, we implemented dctc using cuda based on [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). As you can see below, DCTC can achieve better performance and converge much faster than ctc.

![dctc.png](https://s2.loli.net/2023/09/01/KmO9GhbukEYLWdH.png)

# Experiments
In this repo, we only train the CRNN on the Document split of [Chinese Benchmark Dataset](https://github.com/FudanVI/benchmarking-chinese-text-recognition) and use its codebase. We use one Nvidia A6000 with batch size 512 and 125 epochs. 
All the logs and ckpts are [here](https://pan.baidu.com/s/14R3ZQVlwK-oo4280LVYwgg?pwd=46rp).

|  Model    | Document |
|------|----------------------|
| CRNN (CTC) |            96.474%          |
|CRNN (DCTC) | 97.516% |

## Use DCTC in your project

```python
import dctc

criterion = dctc.DCTC(use_il=False, alpha=0.01)

...
...

targets_dict = {
            'targets': text,
            'target_lengths': length
        }
cost = criterion(logits=preds,
                 targets_dict=targets_dict)

```

## Training instructions

1. perpare Chinese Benchmark Dataset
2. `cd benchmarking-chinese-text-recognition\model\CRNN`
3. modify dataset path in the `train.sh`

If you find it uesful, please cite our paper.

```
@misc{zhang2023selfdistillation,
      title={Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach}, 
      author={Ziyin Zhang and Ning Lu and Minghui Liao and Yongshuai Huang and Cheng Li and Min Wang and Wei Peng},
      year={2023},
      eprint={2308.08806},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

DCTC

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