[Doc] add opendatalab download link (#1753)

* add opendatalab link

* fix

* fix

* ip

---------

Co-authored-by: gaotongxiao <[email protected]>

Author: jorie-peng

docs/en/user_guides/data_prepare/dataset_preparer.md

@@ -39,6 +39,8 @@ python tools/dataset_converters/prepare_dataset.py icdar2015 totaltext --task te
 
 To check the supported datasets of Dataset Preparer, please refer to [Dataset Zoo](./datasetzoo.md). Some of other datasets that need to be prepared manually are listed in [Text Detection](./det.md) and [Text Recognition](./recog.md).
 
+For users in China, more datasets can be downloaded from the opensource dataset platform: [OpenDataLab](https://opendatalab.com/). After downloading the data, you can place the files listed in `data_obtainer.save_name` in `data/cache` and rerun the script.
+
 ## Advanced Usage
 
 ### LMDB Format

docs/en/user_guides/data_prepare/det.md

@@ -46,6 +46,14 @@ This page is a manual preparation guide for datasets not yet supported by [Datas
     # Default output format [None]
   ```
 
+For users in China, these datasets can also be downloaded from [OpenDataLab](https://opendatalab.com/) with high speed:
+
+- [CTW1500](https://opendatalab.com/SCUT-CTW1500?source=OpenMMLab%20GitHub)
+- [ICDAR2013](https://opendatalab.com/ICDAR_2013?source=OpenMMLab%20GitHub)
+- [ICDAR2015](https://opendatalab.com/ICDAR2015?source=OpenMMLab%20GitHub)
+- [Totaltext](https://opendatalab.com/TotalText?source=OpenMMLab%20GitHub)
+- [MSRA-TD500](https://opendatalab.com/MSRA-TD500?source=OpenMMLab%20GitHub)
+
 ## Important Note
 
 ```{note}

docs/en/user_guides/data_prepare/recog.md

@@ -49,6 +49,16 @@ This page is a manual preparation guide for datasets not yet supported by [Datas
     # Default output format [None]
   ```
 
+For users in China, these datasets can also be downloaded from [OpenDataLab](https://opendatalab.com/) with high speed:
+
+- [icdar_2013](https://opendatalab.com/ICDAR_2013?source=OpenMMLab%20GitHub)
+- [icdar_2015](https://opendatalab.com/ICDAR2015?source=OpenMMLab%20GitHub)
+- [IIIT5K](https://opendatalab.com/IIIT_5K?source=OpenMMLab%20GitHub)
+- [ct80](https://opendatalab.com/CUTE_80?source=OpenMMLab%20GitHub)
+- [svt](https://opendatalab.com/SVT?source=OpenMMLab%20GitHub)
+- [Totaltext](https://opendatalab.com/TotalText?source=OpenMMLab%20GitHub)
+- [IAM](https://opendatalab.com/IAM_Handwriting?source=OpenMMLab%20GitHub)
+
 ## ICDAR 2011 (Born-Digital Images)
 
 - Step1: Download `Challenge1_Training_Task3_Images_GT.zip`, `Challenge1_Test_Task3_Images.zip`, and `Challenge1_Test_Task3_GT.txt` from [homepage](https://rrc.cvc.uab.es/?ch=1&com=downloads) `Task 1.3: Word Recognition (2013 edition)`.

docs/zh_cn/user_guides/data_prepare/dataset_preparer.md

@@ -38,6 +38,8 @@ python tools/dataset_converters/prepare_dataset.py icdar2015 totaltext --task te
 
 进一步了解 Dataset Preparer 支持的数据集，您可以浏览[支持的数据集文档](./datasetzoo.md)。一些需要手动准备的数据集也列在了 [文字检测](./det.md) 和 [文字识别](./recog.md) 内。
 
+对于中国境内的用户，我们也推荐通过开源数据平台[OpenDataLab](https://opendatalab.com/)来下载数据，以获得更好的下载体验。数据下载后，参考脚本中 `data_obtainer` 的 `save_name` 字段，将文件放在 `data/cache/` 下并重新运行脚本即可。
+
 ## 进阶用法
 
 ### LMDB 格式

docs/zh_cn/user_guides/data_prepare/det.md

@@ -20,6 +20,14 @@
 |  TextOCR   |  [下载地址](https://textvqa.org/textocr/dataset)  |                       -                       |                         -                          |                       -                        |
 | Totaltext  | [下载地址](https://github.com/cs-chan/Total-Text-Dataset) |                       -                       |                         -                          |                       -                        |
 
+对于中国境内的用户，我们也推荐使用开源数据平台[OpenDataLab](https://opendatalab.com/)来获取这些数据集，以获得更好的下载体验:
+
+- [CTW1500](https://opendatalab.com/SCUT-CTW1500?source=OpenMMLab%20GitHub)
+- [ICDAR2013](https://opendatalab.com/ICDAR_2013?source=OpenMMLab%20GitHub)
+- [ICDAR2015](https://opendatalab.com/ICDAR2015?source=OpenMMLab%20GitHub)
+- [Totaltext](https://opendatalab.com/TotalText?source=OpenMMLab%20GitHub)
+- [MSRA-TD500](https://opendatalab.com/MSRA-TD500?source=OpenMMLab%20GitHub)
+
 ## 重要提醒
 
 ```{note}

docs/zh_cn/user_guides/data_prepare/recog.md

@@ -103,6 +103,16 @@
 
 (\*) 注：由于官方的下载地址已经无法访问，我们提供了一个非官方的地址以供参考，但我们无法保证数据的准确性。
 
+对于中国境内的用户，我们也推荐使用开源数据平台[OpenDataLab](https://opendatalab.com/)来获取这些数据集，以获得更好的下载体验:
+
+- [icdar_2013](https://opendatalab.com/ICDAR_2013?source=OpenMMLab%20GitHub)
+- [icdar_2015](https://opendatalab.com/ICDAR2015?source=OpenMMLab%20GitHub)
+- [IIIT5K](https://opendatalab.com/IIIT_5K?source=OpenMMLab%20GitHub)
+- [ct80](https://opendatalab.com/CUTE_80?source=OpenMMLab%20GitHub)
+- [svt](https://opendatalab.com/SVT?source=OpenMMLab%20GitHub)
+- [Totaltext](https://opendatalab.com/TotalText?source=OpenMMLab%20GitHub)
+- [IAM](https://opendatalab.com/IAM_Handwriting?source=OpenMMLab%20GitHub)
+
 ## 准备步骤
 
 ### ICDAR 2013