diff --git a/ppstructure/table/README.md b/ppstructure/table/README.md
index 05f89360608a2d931d38afbff6452c5e9f5e85fa..fb62a53d141b03367e2a753c495741b44a7f1214 100644
--- a/ppstructure/table/README.md
+++ b/ppstructure/table/README.md
@@ -70,7 +70,7 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
### 2.3 Eval
-The table uses TEDS (Tree-Edit-Distance-based Similarity) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
+The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
```json
{"PMC4289340_004_00.png": [
["", "", "", "", "", "| ", " | ", "", " | ", "", " | ", "
", "", "", "", "| ", " | ", "", " | ", "", " | ", "
", "", "
", "", ""],
@@ -89,6 +89,10 @@ cd PaddleOCR/ppstructure
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
```
+If the PubLatNet eval dataset is used, it will be output
+```bash
+teds: 94.85
+```
### 2.4 Inference
diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md
index 09b73577047cb95343d9e52f89796f14d243bfba..232b34efa0725b4b89e66ea6259d2e96b04d701f 100644
--- a/ppstructure/table/README_ch.md
+++ b/ppstructure/table/README_ch.md
@@ -69,7 +69,7 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
### 2.3 评估
-表格使用 TEDS(Tree-Edit-Distance-based Similarity) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
+表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
```json
{"PMC4289340_004_00.png": [
["", "", "", "", "", "| ", " | ", "", " | ", "", " | ", "
", "", "", "", "| ", " | ", "", " | ", "", " | ", "
", "", "
", "", ""],
@@ -87,6 +87,10 @@ json 中，key为图片名，value为对应的gt，gt是一个由三个item组
cd PaddleOCR/ppstructure
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
```
+如使用PubLatNet评估数据集，将会输出
+```bash
+teds: 94.85
+```
### 2.4 预测