diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 494392029fbcca1df336225e66df3d6aca3ad1f1..f0c16618c0dd0b0f0bcc6a06d6b142a59d58e725 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -21,7 +21,6 @@ PaddleOCR开源的文本检测算法列表：
 - [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
 - [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
 - [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
-- [x]  SDMGR([paper](https://arxiv.org/pdf/2103.14470.pdf))
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
 |模型|骨干网络|precision|recall|Hmean|下载链接|
@@ -33,7 +32,6 @@ PaddleOCR开源的文本检测算法列表：
 |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)|
-|SDMGR|VGG16|-|-|87.11%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 
diff --git a/ppstructure/README.md b/ppstructure/README.md
index 8994cdd46191a0fd4fb1beba2fcad91542e19b50..a09a43299b11dccf99897d5a6c69704191253aaf 100644
--- a/ppstructure/README.md
+++ b/ppstructure/README.md
@@ -159,7 +159,6 @@ After running, each image will have a directory with the same name under the dir
 
 **Model List**
 
-
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
 |en_ppocr_mobile_v2.0_table_structure|Table structure prediction for English table scenarios|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
@@ -184,4 +183,5 @@ OCR and table recognition model
 |en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar)  [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
 |en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
 
+
 If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .
diff --git a/ppstructure/docs/imgs/0.png b/ppstructure/docs/imgs/0.png
new file mode 100644
index 0000000000000000000000000000000000000000..b1e8469f070d73074d9d39c7e5b42d7db1734a14
Binary files /dev/null and b/ppstructure/docs/imgs/0.png differ
diff --git a/ppstructure/docs/kie.md b/ppstructure/docs/kie.md
new file mode 100644
index 0000000000000000000000000000000000000000..67424a46fc6cbae3d6a250ad32b53001ec1cdb81
--- /dev/null
+++ b/ppstructure/docs/kie.md
@@ -0,0 +1,71 @@
+
+
+# 关键信息提取(Key Information Extraction)
+
+本节介绍PaddleOCR中关键信息提取SDMGR方法的快速使用和训练方法。
+
+SDMGR是一个关键信息提取算法，将每个检测到的文本区域分类为预定义的类别，如订单ID、发票号码，金额等。
+
+
+* [1. 快速使用](#1-----)
+* [2. 执行训练](#2-----)
+* [3. 执行评估](#3-----)
+
+<a name="1-----"></a>
+## 1. 快速使用
+
+训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集：
+
+```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
+```
+
+执行预测：
+
+```
+cd PaddleOCR/
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
+python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy  Global.infer_img=../wildreceipt/1.txt
+```
+
+执行预测后的结果保存在`./output/sdmgr_kie/predicts_kie.txt`文件中，可视化结果保存在`/output/sdmgr_kie/kie_results/`目录下。
+
+可视化结果如下图所示：
+[img](./imgs/0.png)
+
+<a name="2-----"></a>
+## 2. 执行训练
+
+创建数据集软链到PaddleOCR/train_data目录下：
+```
+cd PaddleOCR/ && mkdir train_data && cd train_data
+
+ln -s ../../wildreceipt ./
+```
+
+训练采用的配置文件是configs/kie/kie_unet_sdmgr.yml，配置文件中默认训练数据路径是`train_data/wildreceipt`，准备好数据后，可以通过如下指令执行训练：
+```
+python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
+```
+<a name="3-----"></a>
+## 3. 执行评估
+
+```
+python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
+```
+
+
+**参考文献：**
+
+<!-- [ALGORITHM] -->
+
+```bibtex
+@misc{sun2021spatial,
+      title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction},
+      author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang},
+      year={2021},
+      eprint={2103.14470},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
diff --git a/ppstructure/docs/model_list.md b/ppstructure/docs/model_list.md
index 835d39a735462edb0d9f51493ec0529248aeadbf..45004490c1c4b0ea01a5fb409024f1eeb922f1a3 100644
--- a/ppstructure/docs/model_list.md
+++ b/ppstructure/docs/model_list.md
@@ -26,3 +26,9 @@
 | --- | --- | --- | --- |
 |PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
 |PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+
+## 3. KIE模型
+
+|模型名称|模型简介|模型大小|下载地址|
+| --- | --- | --- | --- |
+|SDMGR|关键信息提取模型|-|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|