fix comments

deploy/jeston/readme.md

@@ -10,12 +10,13 @@
 
 1. jeston安装PaddlePaddle
 
-paddlepaddle下载[链接](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+PaddlePaddle下载[链接](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
 请选择适合的您Jetpack版本、cuda版本、trt版本的安装包。
 
 安装命令：
 ```shell
-pip3 install -U paddlepaddle_gpu-*-cp36-cp36m-linux_aarch64.whl
+# 安装paddle，以paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl 为例
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
 ```
 
 
@@ -37,7 +38,7 @@ pip3 install -r requirements.txt
 
 ## 2. 执行预测
 
-从[文档](../../doc/doc_ch/ppocr_introduction.md) 模型库中获取PPOCR模型，下面以PP-OCRv3模型为例，介绍在PPOCR模型在jeston上的使用方式：
+从[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/ppocr_introduction.md#6-%E6%A8%A1%E5%9E%8B%E5%BA%93) 模型库中获取PPOCR模型，下面以PP-OCRv3模型为例，介绍在PPOCR模型在jeston上的使用方式：
 
 下载并解压PP-OCRv3模型
 ```

deploy/jeston/readme_en.md

@@ -10,12 +10,12 @@ You need to prepare a Jeston development hardware. If you need TensorRT, you nee
 
 1. Install PaddlePaddle in Jeston
 
-paddlepaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
-Please select the appropriate installation package for your Jetpack version, cuda version, and trt version.
+The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
+Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.
 
-Install paddlepaddle：
+Install PaddlePaddle：
 ```shell
-pip3 install -U paddlepaddle_gpu-*-cp36-cp36m-linux_aarch64.whl
+pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
 ```
 
 
@@ -36,7 +36,7 @@ pip3 install -r requirements.txt
 
 ## 2. Perform prediction
 
-Obtain the PPOCR model from the [document](../../doc/doc_en/ppocr_introduction_en.md) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on jeston:
+Obtain the PPOCR model from the [document](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/ppocr_introduction.md#6-%E6%A8%A1%E5%9E%8B%E5%BA%93) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on jeston:
 
 Download and unzip the PP-OCRv3 models.
 ```

doc/doc_ch/algorithm_det_east.md

@@ -49,20 +49,6 @@
 <a name="4-1"></a>
 ### 4.1 Python推理
 
-#### 4.1.1 基于训练模型的推理
-
-训练完模型后可直接进行推理，以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ 训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar))，使用方式如下：
-
-```shell
-# 下载模型并解压
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar
-tar xf det_r50_vd_east_v2.0_train.tar
-# 执行预测
-python3 tools/infer_det.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy  Global.infer_img=./doc/imgs_en/img_10.jpg
-```
-
-#### 4.1.2 基于paddle预测引擎的推理
-
 首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ 训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar))，可以使用如下命令进行转换：
 
 ```shell

doc/doc_ch/ppocr_introduction.md

@@ -38,7 +38,7 @@ PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模
 
 #### PP-OCRv3
 
-PP-OCRv3在PP-OCRv2的基础上进一步升级。首先，文本检测采用改进fpn网络结构提升检测精度。
+PP-OCRv3在PP-OCRv2的基础上进一步升级。文本检测采用改进fpn网络结构提升检测精度。
 
 
 <a name="2"></a>

doc/doc_en/algorithm_det_east_en.md

@@ -50,19 +50,6 @@ After the data download is complete, please refer to [Text Detection Training Tu
 <a name="4-1"></a>
 ### 4.1 Python Inference
 
-#### 4.1.2 Inference for Trained Model
-After training, inference can be performed directly. Take [the model trained on the ICDAR2015 English dataset](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar) based on the Resnet50_vd backbone network as an example.
-
-
-```shell
-# download trained model
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar
-tar xf det_r50_vd_east_v2.0_train.tar
-# run inference
-python3 tools/infer_det.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy  Global.infer_img=./doc/imgs_en/img_10.jpg
-```
-
-#### 4.1.2 Inference for Inference Model
 First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert:
 
 ```shell