diff --git a/doc/doc_ch/algorithm_rec_sar.md b/doc/doc_ch/algorithm_rec_sar.md
index aedc16714518b2de220118f755e00c3ba6bc7a5e..b8304313994754480a89d708e39149d67f828c0d 100644
--- a/doc/doc_ch/algorithm_rec_sar.md
+++ b/doc/doc_ch/algorithm_rec_sar.md
@@ -24,7 +24,7 @@
 使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
 
 |模型|骨干网络|配置文件|Acc|下载链接|
-| --- | --- | --- | --- | --- | --- | --- |
+| --- | --- | --- | --- | --- |
 |SAR|ResNet31|[rec_r31_sar.yml](../../configs/rec/rec_r31_sar.yml)|87.20%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar)|
 
 注：除了使用MJSynth和SynthText两个文字识别数据集外，还加入了[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg)数据（提取码：627x），和部分真实数据，具体数据细节可以参考论文。
diff --git a/doc/doc_ch/algorithm_rec_srn.md b/doc/doc_ch/algorithm_rec_srn.md
index b124790761eed875434ecf509f7647fe23d1bc90..ca7961359eb902fafee959b26d02f324aece233a 100644
--- a/doc/doc_ch/algorithm_rec_srn.md
+++ b/doc/doc_ch/algorithm_rec_srn.md
@@ -24,7 +24,7 @@
 使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
 
 |模型|骨干网络|配置文件|Acc|下载链接|
-| --- | --- | --- | --- | --- | --- | --- |
+| --- | --- | --- | --- | --- |
 |SRN|Resnet50_vd_fpn|[rec_r50_fpn_srn.yml](../../configs/rec/rec_r50_fpn_srn.yml)|86.31%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
 
 
diff --git a/doc/doc_en/algorithm_rec_sar_en.md b/doc/doc_en/algorithm_rec_sar_en.md
index c8656f5071358951d3f408f525b4b48c2e89817e..8c1e6dbbfa5cc05da4d7423a535c6db74cf8f4c3 100644
--- a/doc/doc_en/algorithm_rec_sar_en.md
+++ b/doc/doc_en/algorithm_rec_sar_en.md
@@ -24,7 +24,7 @@ Paper:
 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
 
 |Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- | --- | --- |
+| --- | --- | --- | --- | --- |
 |SAR|ResNet31|[rec_r31_sar.yml](../../configs/rec/rec_r31_sar.yml)|87.20%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar)|
 
 Note:In addition to using the two text recognition datasets MJSynth and SynthText, [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg) data (extraction code: 627x), and some real data are used in training, the specific data details can refer to the paper.
diff --git a/doc/doc_en/algorithm_rec_srn_en.md b/doc/doc_en/algorithm_rec_srn_en.md
index ebc4a74ffd0215bd46467b38ac48db160c8ada74..c022a81f9e5797c531c79de7e793d44d9a22552c 100644
--- a/doc/doc_en/algorithm_rec_srn_en.md
+++ b/doc/doc_en/algorithm_rec_srn_en.md
@@ -24,7 +24,7 @@ Paper:
 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
 
 |Model|Backbone|config|Acc|Download link|
-| --- | --- | --- | --- | --- | --- | --- |
+| --- | --- | --- | --- | --- |
 |SRN|Resnet50_vd_fpn|[rec_r50_fpn_srn.yml](../../configs/rec/rec_r50_fpn_srn.yml)|86.31%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|