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diff --git a/deploy/jeston/readme.md b/deploy/jeston/readme.md
index 042932b68e42be086428a6c5319c6fb79ebb7e50..21c35f46b68312b639dc2f8505f568698cd2aed2 100644
--- a/deploy/jeston/readme.md
+++ b/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
```
diff --git a/deploy/jeston/readme_en.md b/deploy/jeston/readme_en.md
index 659ca3d35ac7ab18a9c539562ba7874f4ebccc1e..b4e183503f1d06d2b4ba8dae7fb99d1321681919 100644
--- a/deploy/jeston/readme_en.md
+++ b/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.
```
diff --git a/doc/doc_ch/algorithm_det_east.md b/doc/doc_ch/algorithm_det_east.md
index 1e125fd95bb87a67cba52a603fbd71d4f00e1be7..00236877cc770b18717dd28d140d2cb4025bad77 100644
--- a/doc/doc_ch/algorithm_det_east.md
+++ b/doc/doc_ch/algorithm_det_east.md
@@ -49,20 +49,6 @@
### 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
diff --git a/doc/doc_ch/ppocr_introduction.md b/doc/doc_ch/ppocr_introduction.md
index 2c5f0a2210024c2edb964060f09833fe1f02c320..1e54eecc3f122d99235edffdec196623f1aa3574 100644
--- a/doc/doc_ch/ppocr_introduction.md
+++ b/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。
diff --git a/doc/doc_en/algorithm_det_east_en.md b/doc/doc_en/algorithm_det_east_en.md
index 5de5bef8221117eb1986ec6a6beecadc7f80cbcd..bb6184a998988bd1ffa092ffaff0f14947387c4a 100644
--- a/doc/doc_en/algorithm_det_east_en.md
+++ b/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
### 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