algorithm_det_drrg_en.md

# DRRG

- [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:
> [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://arxiv.org/abs/2003.07493)
> Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng
> CVPR, 2020

On the CTW1500 dataset, the text detection result is as follows:

|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
| --- | --- | --- | --- | --- | --- | --- |
| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[trained model](drrg_model)|

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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 DRRG model is trained using the CTW1500 text detection public dataset. For the download of the dataset, please refer to [ocr_datasets](./dataset/ocr_datasets_en.md).

After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) for training. 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

Since the model needs to be converted to Numpy data for many times in the forward, DRRG dynamic graph to static graph is not supported.

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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{zhang2020deep,
  title={Deep relational reasoning graph network for arbitrary shape text detection},
  author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9699--9708},
  year={2020}
}
```