test=document_fix, improve cpp infer doc

deploy/cpp_infer/readme.md

@@ -194,7 +194,37 @@ sh tools/run.sh
 ```
 
 * 若需要使用方向分类器，则需要将`tools/config.txt`中的`use_angle_cls`参数修改为1，表示开启方向分类器的预测。
+* 更多地，tools/config.txt中的参数及解释如下。
 
+```
+use_gpu  0 # 是否使用GPU，1表示使用，0表示不使用
+gpu_id  0 # GPU id，使用GPU时有效
+gpu_mem  4000  # 申请的GPU内存
+cpu_math_library_num_threads  10 # CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快
+use_mkldnn 1 # 是否使用mkldnn库
+use_zero_copy_run 1 # 是否使用use_zero_copy_run进行预测
+
+# det config
+max_side_len  960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960
+det_db_thresh  0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
+det_db_box_thresh  0.5 # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
+det_db_unclip_ratio  1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本
+det_model_dir  ./inference/det_db # 检测模型inference model地址
+
+# cls config
+use_angle_cls 0 # 是否使用方向分类器，0表示不使用，1表示使用
+cls_model_dir ./inference/cls # 方向分类器inference model地址
+cls_thresh  0.9 # 方向分类器的得分阈值
+
+# rec config
+rec_model_dir  ./inference/rec_crnn # 识别模型inference model地址
+char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # 字典文件
+
+# show the detection results
+visualize 1 # 是否对结果进行可视化，为1时，会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。
+```
+
+* PaddleOCR也支持多语言的预测，更多细节可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分。
 
 最终屏幕上会输出检测结果如下。
 
@@ -205,4 +235,4 @@ sh tools/run.sh
 
 ### 2.3 注意
 
-* C++预测默认未开启MKLDNN(`tools/config.txt`中的`use_mkldnn`设置为0)，如果需要使用MKLDNN进行预测加速，则需要将`use_mkldnn`修改为1，同时使用最新版本的Paddle源码编译预测库。在使用MKLDNN进行CPU预测时，如果同时预测多张图像，则会出现内存泄露的问题（不打开MKLDNN则没有该问题），目前该问题正在修复中，临时解决方案为：预测多张图片时，每隔30张图片左右对识别(`CRNNRecognizer`)和检测类(`DBDetector`)重新初始化一次。
+* 在使用Paddle预测库时，推荐使用2.0.0-beta0版本的预测库。

deploy/cpp_infer/readme_en.md

@@ -202,6 +202,38 @@ sh tools/run.sh
 ```
 
 * If you want to orientation classifier to correct the detected boxes, you can set `use_angle_cls` in the file `tools/config.txt` as 1 to enable the function.
+* What's more, Parameters and their meanings in `tools/config.txt` are as follows.
+
+
+```
+use_gpu  0 # Whether to use GPU, 0 means not to use, 1 means to use
+gpu_id  0 # GPU id when use_gpu is 1
+gpu_mem  4000  # GPU memory requested
+cpu_math_library_num_threads  10 # Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
+use_mkldnn 1 # Whether to use mkdlnn library
+use_zero_copy_run 1 # Whether to use use_zero_copy_run for inference
+
+max_side_len  960 #  Limit the maximum image height and width to 960
+det_db_thresh  0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
+det_db_box_thresh  0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
+det_db_unclip_ratio  1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
+det_model_dir  ./inference/det_db # Address of detection inference model
+
+# cls config
+use_angle_cls 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use
+cls_model_dir ./inference/cls # Address of direction classifier inference model
+cls_thresh  0.9 # Score threshold of the  direction classifier
+
+# rec config
+rec_model_dir  ./inference/rec_crnn # Address of recognition inference model
+char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file
+
+# show the detection results
+visualize 1 # Whether to visualize the results，when it is set as 1, The prediction result will be save in the image file `./ocr_vis.png`.
+```
+
+* Multi-language inference is also supported in PaddleOCR, for more details, please refer to part of multi-language dictionaries and models in [recognition tutorial](../../doc/doc_ch/recognition.md).
+
 
 The detection results will be shown on the screen, which is as follows.
 
@@ -210,6 +242,6 @@ The detection results will be shown on the screen, which is as follows.
 </div>
 
 
-### 2.3 Note
+### 2.3 Notes
 
-* `MKLDNN` is disabled by default for C++ inference (`use_mkldnn` in `tools/config.txt` is set to 0), if you need to use MKLDNN for inference acceleration, you need to modify `use_mkldnn` to 1, and use the latest version of the Paddle source code to compile the inference library. When using MKLDNN for CPU prediction, if multiple images are predicted at the same time, there will be a memory leak problem (the problem is not present if MKLDNN is disabled). The problem is currently being fixed, and the temporary solution is: when predicting multiple pictures, Re-initialize the recognition (`CRNNRecognizer`) and detection class (`DBDetector`) every 30 pictures or so.
+* Paddle2.0.0-beta0 inference model library is recommanded for this tuturial.