Merge pull request #363 from tink2123/sync_en_doc

sync en doc

doc/doc_ch/inference.md

@@ -74,7 +74,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 
 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：
 
-![](imgs_results/det_res_2.jpg)
+![](../imgs_results/det_res_2.jpg)
 
 通过设置参数det_max_side_len的大小，改变检测算法中图片规范化的最大值。当图片的长宽都小于det_max_side_len，则使用原图预测，否则将图片等比例缩放到最大值，进行预测。该参数默认设置为det_max_side_len=960. 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以执行如下命令：
 

doc/doc_en/inference_en.md

 
-# PREDICTION FROM INFERENCE MODEL
+# Reasoning based on Python prediction engine
 
 The inference model (the model saved by fluid.io.save_inference_model) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
 
@@ -18,7 +18,13 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar &
 ```
 The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
 ```
-python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+#  Global.save_inference_dir Set the address where the converted model will be saved.
+
+python3 tools/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy \
+        Global.save_inference_dir=./inference/det_db/
 ```
 When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file.
 `Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved.
@@ -38,6 +44,11 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar
 
 The recognition model is converted to the inference model in the same way as the detection, as follows:
 ```
+# -c Set the training algorithm yml configuration file
+# -o Set optional parameters
+#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+#  Global.save_inference_dir Set the address where the converted model will be saved.
+
 python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
         Global.save_inference_dir=./inference/rec_crnn/
 ```
@@ -53,7 +64,8 @@ After the conversion is successful, there are two files in the directory:
 
 ## TEXT DETECTION MODEL INFERENCE
 
-The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model. Because EAST and DB algorithms are very different, when inference, it is necessary to adapt the EAST text detection algorithm by passing in corresponding parameters.
+The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model.
+Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**.
 
 ### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
 

doc/doc_en/recognition_en.md

@@ -96,6 +96,16 @@ You can use them if needed.
 
 To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
 
+- Custom dictionary
+
+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.
+
+- 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**
+
 ### TRAINING
 
 PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
@@ -122,6 +132,17 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
 ```
 
+- 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.
+
+The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse.
+
+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)
+
+
+- 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.
 
 If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.