fix dead link

ppstructure/table/README.md

@@ -19,7 +19,34 @@ The table ocr flow chart is as follows
 
 
 ### 2.1 Train
-TBD
+
+In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
+
+#### data preparation 
+The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683 ), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
+
+#### Start training 
+*If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
+```shell
+# single GPU training
+python3 tools/train.py -c configs/table/table_mv3.yml
+# multi-GPU training
+# Set the GPU ID used by the '--gpus' parameter.
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
+```
+
+In the above instruction, use `-c` to select the training to use the `configs/table/table_mv3.yml` configuration file.
+For a detailed explanation of the configuration file, please refer to [config](../../doc/doc_en/config_en.md).
+
+#### load trained model and continue training
+
+If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
+
+```shell
+python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
+```
+
+**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
 
 ### 2.2 Eval
 First cd to the PaddleOCR/ppstructure directory

ppstructure/table/README_ch.md

@@ -19,6 +19,8 @@
 
 
 ### 2.1 训练
+在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。
+
 #### 数据准备 
 训练数据使用公开数据集[PubTabNet](https://arxiv.org/abs/1911.10683)，可以从[官网](https://github.com/ibm-aur-nlp/PubTabNet)下载。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
 
@@ -31,7 +33,7 @@ python3 tools/train.py -c configs/table/table_mv3.yml
 python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
 ```
 
-上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](./config.md)。
+上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](../../doc/doc_ch/config.md)。
 
 #### 断点训练