diff --git a/doc/doc_ch/algorithm_det_drrg.md b/doc/doc_ch/algorithm_det_drrg.md
new file mode 100644
index 000000000..b3d533b28
--- /dev/null
+++ b/doc/doc_ch/algorithm_det_drrg.md
@@ -0,0 +1,78 @@
+# DRRG
+
+- [1. 算法简介](#1-算法简介)
+- [2. 环境配置](#2-环境配置)
+- [3. 模型训练、评估、预测](#3-模型训练评估预测)
+- [4. 推理部署](#4-推理部署)
+  - [4.1 Python推理](#41-python推理)
+  - [4.2 C++推理](#42-c推理)
+  - [4.3 Serving服务化部署](#43-serving服务化部署)
+  - [4.4 更多推理部署](#44-更多推理部署)
+- [5. FAQ](#5-faq)
+- [引用](#引用)
+
+<a name="1"></a>
+## 1. 算法简介
+
+论文信息：
+> [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
+
+在CTW1500文本检测公开数据集上，算法复现效果如下：
+
+| 模型  |骨干网络|配置文件|precision|recall|Hmean|下载链接|
+|-----| --- | --- | --- | --- | --- | --- |
+| DRRG | ResNet50_vd | [configs/det/det_r50_drrg_ctw.yml](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[训练模型](drrg_model)|
+
+<a name="2"></a>
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+<a name="3"></a>
+## 3. 模型训练、评估、预测
+
+上述DRRG模型使用CTW1500文本检测公开数据集训练得到，数据集下载可参考 [ocr_datasets](./dataset/ocr_datasets.md)。
+
+数据下载完成后，请参考[文本检测训练教程](./detection.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
+
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 4.1 Python推理
+
+由于模型前向运行时需要多次转换为Numpy数据进行运算，因此DRRG的动态图转静态图暂未支持。
+
+<a name="4-2"></a>
+### 4.2 C++推理
+
+暂未支持
+
+<a name="4-3"></a>
+### 4.3 Serving服务化部署
+
+暂未支持
+
+<a name="4-4"></a>
+### 4.4 更多推理部署
+
+暂未支持
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## 引用
+
+```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}
+}
+```
diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index b9b8cfa67..89ea730ee 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -29,6 +29,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 - [x]  [SAST](./algorithm_det_sast.md)
 - [x]  [PSENet](./algorithm_det_psenet.md)
 - [x]  [FCENet](./algorithm_det_fcenet.md)
+- [x]  [DRRG](./algorithm_det_drrg.md)
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
 
@@ -54,6 +55,7 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |  
 |FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
+|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[训练模型](drrg_model)|
 
 **说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
 * [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
diff --git a/doc/doc_en/algorithm_det_drrg_en.md b/doc/doc_en/algorithm_det_drrg_en.md
new file mode 100644
index 000000000..837f5c200
--- /dev/null
+++ b/doc/doc_en/algorithm_det_drrg_en.md
@@ -0,0 +1,79 @@
+# 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)
+
+<a name="1"></a>
+## 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)|
+
+<a name="2"></a>
+## 2. Environment
+Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md).
+
+
+<a name="3"></a>
+## 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.
+
+<a name="4"></a>
+## 4. Inference and Deployment
+
+<a name="4-1"></a>
+### 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.
+
+<a name="4-2"></a>
+### 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
+@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}
+}
+```
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 073bca103..8722939e9 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -27,6 +27,7 @@ Supported text detection algorithms (Click the link to get the tutorial):
 - [x]  [SAST](./algorithm_det_sast_en.md)
 - [x]  [PSENet](./algorithm_det_psenet_en.md)
 - [x]  [FCENet](./algorithm_det_fcenet_en.md)
+- [x]  [DRRG](./algorithm_det_drrg_en.md)
 
 On the ICDAR2015 dataset, the text detection result is as follows:
 
@@ -52,6 +53,7 @@ On CTW1500 dataset, the text detection result is as follows:
 |Model|Backbone|Precision|Recall|Hmean| Download link|
 | --- | --- | --- | --- | --- |---|  
 |FCE|ResNet50_dcn|88.39%|82.18%|85.27%| [trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar) |
+|DRRG|ResNet50_vd|89.92%|80.91%|85.18%|[trained model](drrg_model)|
 
 **Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
 * [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).