cherry-pick add total text
diff --git a/configs/e2e/e2e_r50_vd_pg.yml b/configs/e2e/e2e_r50_vd_pg.yml
index 3196095587..8128bde15f 100644
--- a/configs/e2e/e2e_r50_vd_pg.yml
+++ b/configs/e2e/e2e_r50_vd_pg.yml
@@ -72,7 +72,7 @@ Train:
   dataset:
     name: PGDataSet
     data_dir: ./train_data/total_text/train
-    label_file_list: [./train_data/total_text/train/total_text.txt]
+    label_file_list: [./train_data/total_text/train/train.txt]
     ratio_list: [1.0]
     transforms:
       - DecodeImage: # load image
@@ -96,7 +96,7 @@ Eval:
   dataset:
     name: PGDataSet
     data_dir: ./train_data/total_text/test
-    label_file_list: [./train_data/total_text/test/total_text.txt]
+    label_file_list: [./train_data/total_text/test/test.txt]
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
diff --git a/doc/doc_ch/pgnet.md b/doc/doc_ch/pgnet.md
index 2658538608..e79c2388ea 100644
--- a/doc/doc_ch/pgnet.md
+++ b/doc/doc_ch/pgnet.md
@@ -83,19 +83,19 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
 本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
 
 ###  准备数据
-下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) 数据集到PaddleOCR/train_data/目录，数据集组织结构：
+下载解压[totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) 数据集到PaddleOCR/train_data/目录，数据集组织结构：
 ```
 /PaddleOCR/train_data/total_text/train/
   |- rgb/            # total_text数据集的训练数据
-      |- gt_0.png
+      |- img11.jpg
       | ...  
-  |- total_text.txt  # total_text数据集的训练标注
+  |- train.txt       # total_text数据集的训练标注
 ```
 
 total_text.txt标注文件格式如下，文件名和标注信息中间用"\t"分隔：
 ```
 " 图像文件名                    json.dumps编码的图像标注信息"
-rgb/gt_0.png    [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}]
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
 ```
 json.dumps编码前的图像标注信息是包含多个字典的list，字典中的 `points` 表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
 `transcription` 表示当前文本框的文字，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
diff --git a/doc/doc_en/pgnet_en.md b/doc/doc_en/pgnet_en.md
index 2ab1116ce7..9c46802b5b 100644
--- a/doc/doc_en/pgnet_en.md
+++ b/doc/doc_en/pgnet_en.md
@@ -76,19 +76,19 @@ The visualized end-to-end results are saved to the `./inference_results` folder
 This section takes the totaltext dataset as an example to introduce the training, evaluation and testing of the end-to-end model in PaddleOCR.
 
 ###  Data Preparation
-Download and unzip [totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) dataset to PaddleOCR/train_data/, dataset organization structure is as follow：
+Download and unzip [totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) dataset to PaddleOCR/train_data/, dataset organization structure is as follow：
 ```
 /PaddleOCR/train_data/total_text/train/
   |- rgb/            # total_text training data of dataset
-      |- gt_0.png
+      |- img11.png
       | ...  
-  |- total_text.txt  # total_text training annotation of dataset
+  |- train.txt       # total_text training annotation of dataset
 ```
 
 total_text.txt: the format of dimension file is as follows，the file name and annotation information are separated by "\t"：
 ```
 " Image file name             Image annotation information encoded by json.dumps"
-rgb/gt_0.png    [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}]
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
 ```
 The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
 
