diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index ef96f6ec122594afd115b333ffc18fb836253b79..cda8b7a9279c0209cee6c16badc7af8c2ccbc953 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -17,7 +17,7 @@
 ### 1.1 文本检测算法
 
 已支持的文本检测算法列表（戳链接获取使用教程）：
-- [x]  [DB](./algorithm_det_db.md)
+- [x]  [DB与DB++](./algorithm_det_db.md)
 - [x]  [EAST](./algorithm_det_east.md)
 - [x]  [SAST](./algorithm_det_sast.md)
 - [x]  [PSENet](./algorithm_det_psenet.md)
@@ -34,6 +34,8 @@
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
 |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
 |PSE|MobileNetV3|82.20%|70.48%|75.89%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|DB|ResNet50|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB++|ResNet50|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 
diff --git a/doc/doc_en/algorithm_det_db_en.md b/doc/doc_en/algorithm_det_db_en.md
index f5f333a039acded88f0f28d302821c5eb10d7402..0bd0152ce327edd5dae8459052c4a6916a5f2ea6 100644
--- a/doc/doc_en/algorithm_det_db_en.md
+++ b/doc/doc_en/algorithm_det_db_en.md
@@ -1,4 +1,4 @@
-# DB
+# DB and DB++
 
 - [1. Introduction](#1)
 - [2. Environment](#2)
@@ -21,13 +21,23 @@ Paper:
 > Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang
 > AAAI, 2020
 
+> [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304)
+> Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang
+> TPAMI, 2022
+
 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](../../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](../../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)|
+|DB++|ResNet50|[configs/det/det_r50_db++_ic15.yml](../../configs/det/det_r50_db++_ic15.yml)|90.89%|82.66%|86.58%|[pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
+
+On the TD_TR dataset, the text detection result is as follows:
 
+|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
+| --- | --- | --- | --- | --- | --- | --- |
+|DB++|ResNet50|[configs/det/det_r50_db++_td_tr.yml](../../configs/det/det_r50_db++_td_tr.yml)|92.92%|86.48%|89.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_td_tr_train.tar)|
 
 <a name="2"></a>
 ## 2. Environment
@@ -96,4 +106,12 @@ More deployment schemes supported for DB:
   pages={11474--11481},
   year={2020}
 }
-```
\ No newline at end of file
+
+@article{liao2022real,
+  title={Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion},
+  author={Liao, Minghui and Zou, Zhisheng and Wan, Zhaoyi and Yao, Cong and Bai, Xiang},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  year={2022},
+  publisher={IEEE}
+}
+```
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index bc96cdf2351f10454441e20d319e485019bbec00..d8f9428aae184af97dee1fa1d68f8f1a9a682458 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -17,7 +17,7 @@ This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the mo
 ### 1.1 Text Detection Algorithms
 
 Supported text detection algorithms (Click the link to get the tutorial):
-- [x]  [DB](./algorithm_det_db_en.md)
+- [x]  [DB and DB++](./algorithm_det_db_en.md)
 - [x]  [EAST](./algorithm_det_east_en.md)
 - [x]  [SAST](./algorithm_det_sast_en.md)
 - [x]  [PSENet](./algorithm_det_psenet_en.md)
@@ -34,6 +34,7 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
 |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
 |PSE|MobileNetV3|82.20%|70.48%|75.89%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|DB++|ResNet50|90.89%|82.66%|86.58%|[合成数据预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams)/[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar)|
 
 On Total-Text dataset, the text detection result is as follows: