diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
index 1b8554b..805414d 100644
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@@ -23,9 +23,8 @@ inference 模型（`paddle.jit.save`保存的模型）
- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
- - [4. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理)
- - [5. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- - [6. 多语言模型的推理](#多语言模型的推理)
+ - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+ - [5. 多语言模型的推理](#多语言模型的推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
@@ -295,20 +294,7 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
-
-### 4. 基于SRN损失的识别模型推理
-
-基于SRN损失的识别模型需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256"
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
- --rec_model_dir="./inference/srn/" \
- --rec_image_shape="1, 64, 256" \
- --rec_char_type="en"
-```
-
-
-### 5. 自定义文本识别字典的推理
+### 4. 自定义文本识别字典的推理
如果训练时修改了文本的字典，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
```
@@ -316,7 +302,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
-### 6. 多语言模型的推理
+### 5. 多语言模型的推理
如果您需要预测的是其他语言模型，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/` 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_en.md b/doc/doc_en/inference_en.md
index 31f6b1e..8ce0ea4 100644
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -26,9 +26,8 @@ Next, we first introduce how to convert a trained model into an inference model,
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
- [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
- - [4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
- - [5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
- - [6. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
+ - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
+ - [5. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
- [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
@@ -296,21 +295,8 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
-
-### 4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
-
-The recognition model based on SRN need to ensure that the predicted shape is consistent with the training, such as: --rec_image_shape="1, 64, 256"
-
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
- --rec_model_dir="./inference/srn/" \
- --rec_image_shape="1, 64, 256" \
- --rec_char_type="en"
-```
-
-
-### 5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
```
@@ -318,7 +304,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
-### 6. MULTILINGAUL MODEL INFERENCE
+### 5. MULTILINGAUL MODEL INFERENCE
If you need to predict other language models, 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/` path, such as Korean recognition: