algorithm_rec_can_en.md

# RobustScanner

- [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

Paper:
> [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://arxiv.org/abs/2207.11463)
> Bohan Li, Ye Yuan, Dingkang Liang, Xiao Liu, Zhilong Ji, Jinfeng Bai, Wenyu Liu, Xiang Bai
> ECCV, 2022

Using CROHME handwrittem mathematical expression recognition datasets for training, and evaluating on its test sets, the algorithm reproduction effect is as follows:

|Model|Backbone|config|exprate|Download link|
| --- | --- | --- | --- | --- |
|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|coming soon|

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## 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_d28_can.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_d28_can.yml
```

Evaluation:

```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy
```

Prediction:

```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy
```

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## 4. Inference and Deployment

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### 4.1 Python Inference
First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert:

```
python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
```

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

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_image_shape="1, 132, 519" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
```

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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

<a name="5"></a>
## 5. FAQ


## Citation

```bibtex
@misc{https://doi.org/10.48550/arxiv.2207.11463,
  doi = {10.48550/ARXIV.2207.11463},
  url = {https://arxiv.org/abs/2207.11463},
  author = {Li, Bohan and Yuan, Ye and Liang, Dingkang and Liu, Xiao and Ji, Zhilong and Bai, Jinfeng and Liu, Wenyu and Bai, Xiang},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```