diff --git a/doc/doc_ch/algorithm_det_db.md b/doc/doc_ch/algorithm_det_db.md
index fc887743bcdb4cf6e29ac4d8e643dda9520e4795..7f94ceaee06ac41a42c785f26bffa30005a98355 100644
--- a/doc/doc_ch/algorithm_det_db.md
+++ b/doc/doc_ch/algorithm_det_db.md
@@ -47,13 +47,13 @@
 ### 4.1 Python推理
 首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
 
-```
+```shell
 python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
 ```
 
 DB文本检测模型推理，可以执行如下命令：
 
-```
+```shell
 python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
 ```
 
@@ -65,15 +65,20 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 
 <a name="4-2"></a>
 ### 4.2 C++推理
-敬请期待
+
+准备好推理模型后，参考[cpp infer](../../deploy/cpp_infer/)教程进行操作即可。
 
 <a name="4-3"></a>
 ### 4.3 Serving服务化部署
-敬请期待
+
+准备好推理模型后，参考[pdserving](../../deploy/pdserving/)教程进行Serving服务化部署，包括Python Serving和C++ Serving两种模式。
 
 <a name="4-4"></a>
 ### 4.4 更多推理部署
-敬请期待
+
+DB模型还支持以下推理部署方式：
+
+- Paddle2ONNX推理：准备好推理模型后，参考[paddle2onnx](../../deploy/paddle2onnx/)教程操作。
 
 <a name="5"></a>
 ## 5. FAQ
diff --git a/doc/doc_en/algorithm_det_db_en.md b/doc/doc_en/algorithm_det_db_en.md
index 40ba022f84786f3538f348e350a41506962c9c2c..b387a8ec217b351164d7cac878539bab19157a6e 100644
--- a/doc/doc_en/algorithm_det_db_en.md
+++ b/doc/doc_en/algorithm_det_db_en.md
@@ -14,4 +14,86 @@
 - [5. FAQ](#5)
 
 <a name="1"></a>
-## 1. Introduction
\ No newline at end of file
+## 1. Introduction
+
+Paper:
+> [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947)
+> Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
+> AAAI, 2020
+
+On the ICDAR2015 dataset, the text detection result is as follows:
+
+|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+
+
+<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
+
+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.
+
+<a name="4"></a>
+## 4. Inference and Deployment
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
+
+```shell
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
+```
+
+DB text detection model inference, you can execute the following command:
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
+```
+
+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_img_10_db.jpg)
+
+**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.
+
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+With the inference model prepared, refer to the [cpp infer](../../deploy/cpp_infer/) tutorial for C++ inference.
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+With the inference model prepared, refer to the [pdserving](../../deploy/pdserving/) tutorial for service deployment by Paddle Serving.
+
+<a name="4-4"></a>
+### 4.4 More
+
+More deployment schemes supported for DB:
+
+- Paddle2ONNX: with the inference model prepared, please refer to the [paddle2onnx](../../deploy/paddle2onnx/) tutorial.
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@inproceedings{liao2020real,
+  title={Real-time scene text detection with differentiable binarization},
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={34},
+  number={07},
+  pages={11474--11481},
+  year={2020}
+}
+```
\ No newline at end of file