diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
index 4869eb2ce45cc86434ecbb0c29c6ed4fe90ab9e9..663533c492ab5dc0bd22cc79bd95c9d1d194d854 100644
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@@ -186,7 +186,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
-
+(coming soon)
**注意**：本代码库中，EAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
@@ -205,7 +205,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
-
+(coming soon)
#### (2). 弯曲文本检测模型（Total-Text）
首先将SAST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Total-Text英文数据集训练的模型为例（[模型下载地址(coming soon)](link))，可以使用如下命令进行转换：
@@ -221,7 +221,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
-
+(coming soon)
**注意**：本代码库中，SAST后处理Locality-Aware NMS有python和c++两种版本，c++版速度明显快于python版。由于c++版本nms编译版本问题，只有python3.5环境下会调用c++版nms，其他情况将调用python版nms。
@@ -245,8 +245,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
-Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
-
+```bash
+Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
+```
### 2. 基于CTC损失的识别模型推理
@@ -281,7 +282,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
执行命令后，上面图像的识别结果如下：
-Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]
+```bash
+Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
+```
**注意**：由于上述模型是参考[DTRB](https://arxiv.org/abs/1904.01906)文本识别训练和评估流程，与超轻量级中文识别模型训练有两方面不同：
@@ -313,9 +316,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
执行命令后，上图的预测结果为：
``` text
-2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
-2020-09-19 16:15:05,077-INFO: word : 바탕으로
-2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
+Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
```
@@ -378,4 +379,4 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d
执行命令后，识别结果图像如下：
-
+(coming soon)
diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
index 12553c4cf3bc83f1f4503bfadf59fff9f7d8a8a7..411a733dd062cf347d7a2e5d5d067739bda36819 100644
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -192,7 +192,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
-
+(coming soon)
**Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
@@ -214,7 +214,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
-
+(coming soon)
#### (2). Curved text detection model (Total-Text)
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link (coming soon)](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
@@ -231,7 +231,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
-
+(coming soon)
**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
@@ -254,8 +254,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
-Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
-
+```bash
+Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
+```
### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE
@@ -276,7 +277,6 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
-
The recognition model based on Attention loss is different from ctc, and additional recognition algorithm parameters need to be set --rec_algorithm="RARE"
After executing the command, the recognition result of the above image is as follows:
@@ -284,8 +284,13 @@ After executing the command, the recognition result of the above image is as fol
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
```
-Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]
+
+After executing the command, the recognition result of the above image is as follows:
+
+```bash
+Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
+```
**Note**：Since the above model refers to [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the training of lightweight Chinese recognition model in two aspects:
- The image resolution used in training is different: the image resolution used in training the above model is [3，32，100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`.
@@ -318,9 +323,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
After executing the command, the prediction result of the above figure is:
``` text
-2020-09-19 16:15:05,076-INFO: index: [205 206 38 39]
-2020-09-19 16:15:05,077-INFO: word : 바탕으로
-2020-09-19 16:15:05,077-INFO: score: 0.9171358942985535
+Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
```
@@ -381,4 +384,4 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d
After executing the command, the recognition result image is as follows:
-
+(coming soon)
diff --git a/doc/imgs_results/2.jpg b/doc/imgs_results/2.jpg
index 201ef9ee492702118cd1638ed8a0b832c1c6d9ed..99f7e63b02556506dadf8d838eee22534d21d82c 100644
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diff --git a/doc/imgs_results/det_res_2.jpg b/doc/imgs_results/det_res_2.jpg
index aebcd8ccaca02db7ed4a09cd63ade422abc4735f..c0ae501a7aff7807f53b743745005653775b0d03 100644
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diff --git a/doc/imgs_results/det_res_img_10_db.jpg b/doc/imgs_results/det_res_img_10_db.jpg
index bde1585cb50137ae1fd33ce7edfa59e7224ddc96..6af89f6bb32191c361c439c9d26e0239b5392fd9 100644
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