fix typo and add srn for inference,test=document_fix

doc/doc_ch/inference.md

@@ -23,8 +23,9 @@ inference 模型（`fluid.io.save_inference_model`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. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+    - [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. 多语言模型的推理](#多语言模型的推理)
+    - [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)
 
 - [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)
@@ -297,9 +298,21 @@ Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]
 self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
+<a name="基于SRN损失的识别模型推理"></a>
+### 4. 基于SRN损失的识别模型推理
+
+基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。 同时需要保证预测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" \
+                                   --rec_algorithm="SRN"
+```
 
 <a name="自定义文本识别字典的推理"></a>
-### 4. 自定义文本识别字典的推理
+### 5. 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
 
 ```
@@ -307,7 +320,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 ```
 
 <a name="多语言模型的推理"></a>
-### 5. 多语言模型的推理
+### 6. 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：
 

doc/doc_ch/recognition.md

@@ -1,5 +1,24 @@
 ## 文字识别
 
+
+- [一、数据准备](#数据准备)
+    - [数据下载](#数据下载)
+    - [自定义数据集](#自定义数据集)  
+    - [字典](#字典)  
+    - [支持空格](#支持空格)
+
+- [二、启动训练](#文本检测模型推理)
+    - [1. 数据增强](#数据增强)
+    - [2. 训练](#训练)
+    - [3. 小语种](#小语种)
+
+- [三、评估](#评估)
+
+- [四、预测](#预测)
+    - [1. 训练引擎预测](#训练引擎预测)
+
+
+<a name="数据准备"></a>
 ### 数据准备
 
 
@@ -13,13 +32,14 @@ PaddleOCR 支持两种数据格式: `lmdb` 用于训练公开数据，调试算
 ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 ```
 
-
+<a name="数据下载"></a>
 * 数据下载
 
 若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
 
 如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
 
+<a name="自定义数据集"></a>
 * 使用自己数据集
 
 若您希望使用自己的数据进行训练，请参考下文组织您的数据。
@@ -78,7 +98,7 @@ python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_
             |- word_003.jpg
             | ...
 ```
-
+<a name="字典"></a>
 - 字典
 
 最后需要提供一个字典（{word_dict_name}.txt），使模型在训练时，可以将所有出现的字符映射为字典的索引。
@@ -119,13 +139,14 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 如需自定义dic文件，请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
 并将 `character_type` 设置为 `ch`。
 
+<a name="支持空格"></a>
 - 添加空格类别
 
 如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `true`。
 
 **注意：`use_space_char` 仅在 `character_type=ch` 时生效**
 
-
+<a name="启动训练"></a>
 ### 启动训练
 
 PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN 识别模型为例：
@@ -151,7 +172,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 # 训练icdar15英文数据 并将训练日志保存为 tain_rec.log
 python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
 ```
-
+<a name="数据增强"></a>
 - 数据增强
 
 PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
@@ -162,6 +183,7 @@ PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入
 
 *由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
 
+<a name="训练"></a>
 - 训练
 
 PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/rec_CRNN/best_accuracy` 。
@@ -224,17 +246,18 @@ Optimizer:
 ```
 **注意，预测/评估时的配置文件请务必与训练一致。**
 
+<a name="小语种"></a>
 - 小语种
 
 PaddleOCR也提供了多语言的， `configs/rec/multi_languages` 路径下的提供了多语言的配置文件，目前PaddleOCR支持的多语言算法有：
 
-| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language ｜
+| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language |
 | :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | 
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语   ｜
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语   |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语   ｜
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   ｜
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语   ｜
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  | 
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语   ｜
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  | 
 
 多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
 
@@ -269,7 +292,7 @@ TrainReader:
 
 ...
 ```
-
+<a name="评估"></a>
 ### 评估
 
 评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml`  修改EvalReader中的 `label_file_path` 设置。
@@ -281,8 +304,10 @@ export CUDA_VISIBLE_DEVICES=0
 python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
 ```
 
+<a name="预测"></a>
 ### 预测
 
+<a name="训练引擎预测"></a>
 * 训练引擎的预测
 
 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。

doc/doc_en/inference_en.md

@@ -26,7 +26,9 @@ 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. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
+    - [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)
     
 - [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
     - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
@@ -299,17 +301,31 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
 
+<a name="SRN-BASED_RECOGNITION"></a>
+### 4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
+
+The recognition model based on SRN requires additional setting of the recognition algorithm parameter --rec_algorithm="SRN". 
+At the same time, it is necessary 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" \
+                                    --rec_algorithm="SRN"
+```
+
+
 <a name="USING_CUSTOM_CHARACTERS"></a>
-### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 5. 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.
 
 ```
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
 ```
 
-<a name="Multilingual model inference"></a>
+<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
-
+### 6. MULTILINGAUL MODEL INFERENCE
-### 5. Multilingual Model Reasoning
 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:
 

doc/doc_en/recognition_en.md

@@ -1,5 +1,22 @@
 ## TEXT RECOGNITION
 
+- [DATA PREPARATION](#DATA_PREPARATION)
+    - [Dataset Download](#Dataset_download)
+    - [Costom Dataset](#Costom_Dataset)  
+    - [Dictionary](#Dictionary)  
+    - [Add Space Category](#Add_space_category)
+
+- [TRAINING](#TRAINING)
+    - [Data Augmentation](#Data_Augmentation)
+    - [Training](#Training)
+    - [Multi-language](#Multi_language)
+
+- [EVALUATION](#EVALUATION)
+
+- [PREDICTION](#PREDICTION)
+    - [Training engine prediction](#Training_engine_prediction)
+
+<a name="DATA_PREPARATION"></a>
 ### DATA PREPARATION
 
 
@@ -13,13 +30,14 @@ The default storage path for training data is `PaddleOCR/train_data`, if you alr
 ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 ```
 
-
+<a name="Dataset_download"></a>
 * Dataset download
 
 If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，download the lmdb format dataset required for benchmark
 
 If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.
 
+<a name="Costom_Dataset"></a>
 * Use your own dataset:
 
 If you want to use your own data for training, please refer to the following to organize your data.
@@ -72,7 +90,7 @@ Similar to the training set, the test set also needs to be provided a folder con
             |- word_003.jpg
             | ...
 ```
-
+<a name="Dictionary"></a>
 - Dictionary
 
 Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.
@@ -114,12 +132,14 @@ To customize the dict file, please modify the `character_dict_path` field in `co
 
 If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.
 
+<a name="Add_space_category"></a>
 - Add space category
 
 If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `true`.
 
 **Note: use_space_char only takes effect when character_type=ch**
 
+<a name="TRAINING"></a>
 ### TRAINING
 
 PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
@@ -143,7 +163,7 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 # Training icdar15 English data and saving the log as train_rec.log
 python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log
 ```
-
+<a name="Data_Augmentation"></a>
 - Data Augmentation
 
 PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
@@ -152,7 +172,7 @@ The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, rand
 
 Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
 
-
+<a name="Training"></a>
 - Training
 
 PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process.
@@ -215,7 +235,8 @@ Optimizer:
 ```
 **Note that the configuration file for prediction/evaluation must be consistent with the training.**
 
--Minor language
+<a name="Multi_language"></a>
+- Multi-language
 
 PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are:
 
@@ -250,7 +271,7 @@ Global:
 ...
 ```
 
-
+<a name="EVALUATION"></a>
 ### EVALUATION
 
 The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
@@ -261,8 +282,10 @@ export CUDA_VISIBLE_DEVICES=0
 python3 tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy
 ```
 
+<a name="PREDICTION"></a>
 ### PREDICTION
 
+<a name="Training_engine_prediction"></a>
 * Training engine prediction
 
 Using the model trained by paddleocr, you can quickly get prediction through the following script.