# CT

- [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:
> [CentripetalText: An Efficient Text Instance Representation for Scene Text Detection](https://arxiv.org/abs/2107.05945)
> Tao Sheng, Jie Chen, Zhouhui Lian
> NeurIPS, 2021


On the Total-Text dataset, the text detection result is as follows:

|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
| --- | --- | --- | --- | --- | --- | --- |
|CT|ResNet18_vd|[configs/det/det_r18_vd_ct.yml](../../configs/det/det_r18_vd_ct.yml)|88.68%|81.70%|85.05%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_train.tar)|


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## 2. Environment
Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md).


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## 3. Model Training / Evaluation / Prediction


The above CT model is trained using the Total-Text text detection public dataset. For the download of the dataset, please refer to [Total-Text-Dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset). PaddleOCR format annotation download link [train.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/train.txt), [test.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/test.txt).


Please refer to [text detection training tutorial](./detection_en.md). PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different detection models.

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## 4. Inference and Deployment

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### 4.1 Python Inference
First, convert the model saved in the CT text detection training process into an inference model. Taking the model based on the Resnet18_vd backbone network and trained on the Total Text English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_train.tar)), you can use the following command to convert:

```shell
python3 tools/export_model.py -c configs/det/det_r18_vd_ct.yml -o Global.pretrained_model=./det_r18_ct_train/best_accuracy  Global.save_inference_dir=./inference/det_ct
```

CT text detection model inference, you can execute the following command:

```shell
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_ct/" --det_algorithm="CT"
```

The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

![](../imgs_results/det_res_img623_ct.jpg)


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### 4.2 C++ Inference

Not supported

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### 4.3 Serving

Not supported

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### 4.4 More

Not supported

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## 5. FAQ


## Citation

```bibtex
@inproceedings{sheng2021centripetaltext,
    title={CentripetalText: An Efficient Text Instance Representation for Scene Text Detection},
    author={Tao Sheng and Jie Chen and Zhouhui Lian},
    booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
    year={2021}
}
```