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# ABINet

## 1. Introduction

Paper:
> [ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf)
> Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang
> CVPR, 2021

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|
| --- | --- | --- | --- | --- |
|ABINet|ResNet45|[rec_r45_abinet.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/{{PADDLEOCR_GITHUB_REF}}/configs/rec/rec_r45_abinet.yml)|90.75%|[pretrained & trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)|

## 2. Environment

Please refer to ["Environment Preparation"](../../ppocr/environment.en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](../../ppocr/blog/clone.en.md)to clone the project code.

## 3. Model Training / Evaluation / Prediction

Please refer to [Text Recognition Tutorial](../../ppocr/model_train/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:

```bash linenums="1"
# Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r45_abinet.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_r45_abinet.yml
```

### Evaluation

```bash linenums="1"
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r45_abinet.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```

### Prediction

```bash linenums="1"
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r45_abinet.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_r45_abinet_train/best_accuracy
```

## 4. Inference and Deployment

### 4.1 Python Inference

First, the model saved during the ABINet text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)) ), you can use the following command to convert:

```bash linenums="1"
python3 tools/export_model.py -c configs/rec/rec_r45_abinet.yml -o Global.pretrained_model=./rec_r45_abinet_train/best_accuracy  Global.save_inference_dir=./inference/rec_r45_abinet
```

**Note:**

- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
- If you modified the input size during training, please modify the `infer_shape` corresponding to ABINet in the `tools/export_model.py` file.

After the conversion is successful, there are three files in the directory:

```text linenums="1"
/inference/rec_r45_abinet/
    ├── inference.pdiparams
    ├── inference.pdiparams.info
    └── inference.pdmodel
```

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

```bash linenums="1"
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_r45_abinet/' --rec_algorithm='ABINet' --rec_image_shape='3,32,128' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt'
```

![img](./images/word_10.png)

After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
The result is as follows:

```bash linenums="1"
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999995231628418)
```

### 4.2 C++ Inference

Not supported

### 4.3 Serving

Not supported

### 4.4 More

Not supported

## 5. FAQ

1. Note that the MJSynth and SynthText datasets come from [ABINet repo](https://github.com/FangShancheng/ABINet).
2. We use the pre-trained model provided by the ABINet authors for finetune training.

## Citation

```bibtex
@article{Fang2021ABINet,
  title     = {ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
  author    = {Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang},
  booktitle = {CVPR},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.06495},
  pages     = {7098-7107}
}
```
