# SATRN

- [1. Introduction](#1)
- [2. Environment](#2)
- [3. Model Training / Evaluation / Prediction](#3)
    - [3.1 Training](#3-1)
    - [3.2 Evaluation](#3-2)
    - [3.3 Prediction](#3-3)
- [4. Inference and Deployment](#4)
    - [4.1 Python Inference](#4-1)
    - [4.2 C++ Inference](#4-2)
    - [4.3 Serving](#4-3)
    - [4.4 More](#4-4)
- [5. FAQ](#5)

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## 1. Introduction

论文信息：
> [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396)
> Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee
> CVPR, 2020
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:

|Model|Backbone|config|Acc|Download link|
| --- | --- | --- | --- | --- |
|SATRN|ShallowCNN|88.05%|[configs/rec/rec_satrn.yml](../../configs/rec/rec_satrn.yml)|[训练模型](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a)|


<a name="2"></a>
## 2. Environment
Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.


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## 3. Model Training / Evaluation / Prediction

Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.

Training:

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

```
#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_satrn.yml
#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_satrn.yml
```

Evaluation:

```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```

Prediction:

```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
```

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## 4. Inference and Deployment

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### 4.1 Python Inference
First, the model saved during the SATRN text recognition training process is converted into an inference model. ( [Model download link](https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a) ), you can use the following command to convert:

```
python3 tools/export_model.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model=./rec_satrn_train/best_accuracy  Global.save_inference_dir=./inference/rec_satrn
```

For SATRN text recognition model inference, the following commands can be executed:

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_satrn/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SATRN" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
```

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### 4.2 C++ Inference

Not supported

<a name="4-3"></a>
### 4.3 Serving

Not supported

<a name="4-4"></a>
### 4.4 More

Not supported

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## 5. FAQ

## 引用

```bibtex
@article{lee2019recognizing,
      title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
      author={Junyeop Lee and Sungrae Park and Jeonghun Baek and Seong Joon Oh and Seonghyeon Kim and Hwalsuk Lee},
      year={2019},
      eprint={1910.04396},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```