diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md
index 472c0003f963d6f65fa2d0babbf6b9c7d0ec9b80..622ac995d37ce290ee51af06164b0c2aef8b5a14 100644
--- a/doc/doc_ch/inference_ppocr.md
+++ b/doc/doc_ch/inference_ppocr.md
@@ -7,7 +7,8 @@
   - [1. 文本检测模型推理](#1-文本检测模型推理)
   - [2. 文本识别模型推理](#2-文本识别模型推理)
     - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理)
-    - [2.2 多语言模型的推理](#22-多语言模型的推理)
+    - [2.2 英文识别模型推理](#22-英文识别模型推理)
+    - [2.3 多语言模型的推理](#23-多语言模型的推理)
   - [3. 方向分类模型推理](#3-方向分类模型推理)
   - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理)
 
@@ -78,9 +79,29 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
 Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379)
 ```
 
+<a name="英文识别模型推理"></a>
+
+### 2.2 英文识别模型推理
+
+英文识别模型推理，可以执行如下命令， 注意修改字典路径：
+
+```
+# 下载英文数字识别模型：
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
+tar xf en_PP-OCRv3_det_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
+```
+
+![](../imgs_words/en/word_1.png)
+
+执行命令后，上图的预测结果为：
+
+```
+Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
+```
 <a name="多语言模型的推理"></a>
 
-### 2.2 多语言模型的推理
+### 2.3 多语言模型的推理
 
 如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
 ```
diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
index 935f92f5144f582630a45edcc886b609ecdc82da..0f57b0ba6b226c19ecb1e0b60afdfa34302b8e78 100755
--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -8,7 +8,8 @@ This article introduces the use of the Python inference engine for the PP-OCR mo
   - [Text Detection Model Inference](#text-detection-model-inference)
   - [Text Recognition Model Inference](#text-recognition-model-inference)
     - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference)
-    - [2. Multilingual Model Inference](#2-multilingual-model-inference)
+    - [2. English Recognition Model Inference](#2-english-recognition-model-inference)
+    - [3. Multilingual Model Inference](#3-multilingual-model-inference)
   - [Angle Classification Model Inference](#angle-classification-model-inference)
   - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation)
 
@@ -76,10 +77,31 @@ After executing the command, the prediction results (recognized text and score)
 ```bash
 Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671)
 ```
+<a name="2-english-recognition-model-inference"></a>
+### 2. English Recognition Model Inference
 
-<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
+For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by `--rec_char_dict_path`:
 
-### 2. Multilingual Model Inference
+```
+# download en model：
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar
+tar xf en_PP-OCRv3_det_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt"
+```
+
+![](../imgs_words/en/word_1.png)
+
+
+After executing the command, the prediction result of the above figure is:
+
+```
+Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521)
+```
+
+
+<a name="3-multilingual-model-inference"></a>
+
+### 3. Multilingual Model Inference
 If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: