@@ -120,8 +120,8 @@ For a new language request, please refer to [Guideline for new language_requests
- [PP-Structure: Information Extraction](./ppstructure/README.md)
- [Layout Parser](./ppstructure/layout/README.md)
- [Table Recognition](./ppstructure/table/README.md)
- - [DocVQA](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4/ppstructure/vqa)
- - [Key Information Extraction](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/ppstructure/docs/kie.md)
+ - [DocVQA](./ppstructure/vqa/README.md)
+ - [Key Information Extraction](./ppstructure/docs/kie.md)
- Academic Circles
- [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PGNet Algorithm](./doc/doc_en/pgnet_en.md)
diff --git a/README_ch.md b/README_ch.md
index d16b9c0ebff5b47238a7cc246edc4534e54f3efe..f39f3cb9acdb921773368170cae355bd9d29d4ce 100755
--- a/README_ch.md
+++ b/README_ch.md
@@ -99,8 +99,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [PP-Structure信息提取](./ppstructure/README_ch.md)
- [版面分析](./ppstructure/layout/README_ch.md)
- [表格识别](./ppstructure/table/README_ch.md)
- - [DocVQA](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4/ppstructure/vqa)
- - [关键信息提取](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/ppstructure/docs/kie.md)
+ - [DocVQA](./ppstructure/vqa/README_ch.md)
+ - [关键信息提取](./ppstructure/docs/kie.md)
- OCR学术圈
- [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
- [端到端PGNet算法](./doc/doc_ch/pgnet.md)
diff --git a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
index b474d8886a10746b8ac181085c62481dfe7a4229..8bcd79b95b322a38dcd56d6ffe3a203d3d1ea6ae 100644
--- a/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
+++ b/deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java
@@ -160,6 +160,7 @@ public class Predictor {
for (String content : contents) {
wordLabels.add(content);
}
+ wordLabels.add(" ");
Log.i(TAG, "Word label size: " + wordLabels.size());
} catch (Exception e) {
Log.e(TAG, e.getMessage());
diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md
index 4daa73453507959ea10e21a7383d03d00aedf438..8c5a323af40e64f77e76cba23fd5c4408c643de5 100644
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@@ -1,9 +1,8 @@
# Server-side C++ Inference
-This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
-C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used.
-This section will introduce how to configure the C++ environment and complete it in the Linux\Windows (CPU\GPU) environment
-PaddleOCR model deployment.
+This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
+C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used.
+This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to [Windows](./docs/windows_vs2019_build.md) compilation guidelines.
## 1. Prepare the Environment
@@ -15,7 +14,7 @@ PaddleOCR model deployment.
### 1.1 Compile OpenCV
-* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
+* First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.
```bash
cd deploy/cpp_infer
@@ -23,9 +22,9 @@ wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz
```
-Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
+Finally, you will see the folder of `opencv-3.4.7/` in the current directory.
-* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the opencv source code path and compile it in the following way.
+* Compile OpenCV, the OpenCV source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the OpenCV source code path and compile it in the following way.
```shell
@@ -58,11 +57,11 @@ make -j
make install
```
-Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. After `make install` is completed, the opencv header file and library file will be generated in this folder for later OCR source code compilation.
+In the above commands, `root_path` is the downloaded OpenCV source code path, and `install_path` is the installation path of OpenCV. After `make install` is completed, the OpenCV header file and library file will be generated in this folder for later OCR source code compilation.
-The final file structure under the opencv installation path is as follows.
+The final file structure under the OpenCV installation path is as follows.
```
opencv3/
@@ -79,20 +78,20 @@ opencv3/
#### 1.2.1 Direct download and installation
-[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can view and select the appropriate version of the inference library on the official website.
+[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
-* After downloading, use the following method to uncompress.
+* After downloading, use the following command to extract files.
```
tar -xf paddle_inference.tgz
```
-Finally you can see the following files in the folder of `paddle_inference/`.
+Finally you will see the the folder of `paddle_inference/` in the current path.
-#### 1.2.2 Compile from the source code
-* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
-* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
+#### 1.2.2 Compile the inference source code
+* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
+* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from GitHub, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
```shell
@@ -100,7 +99,7 @@ git clone https://github.com/PaddlePaddle/Paddle.git
git checkout develop
```
-* After entering the Paddle directory, the commands to compile the paddle inference library are as follows.
+* Enter the Paddle directory and run the following commands to compile the paddle inference library.
```shell
rm -rf build
@@ -133,14 +132,14 @@ build/paddle_inference_install_dir/
|-- version.txt
```
-Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
+`paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
## 2. Compile and Run the Demo
### 2.1 Export the inference model
-* You can refer to [Model inference](../../doc/doc_ch/inference.md)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
+* You can refer to [Model inference](../../doc/doc_ch/inference.md) and export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
```
inference/
@@ -171,20 +170,28 @@ CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir
```
-`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
+`OPENCV_DIR` is the OpenCV installation path; `LIB_DIR` is the download (`paddle_inference` folder)
or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
-`CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
+`CUDA_LIB_DIR` is the CUDA library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cuDNN library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
* After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
### Run the demo
-Execute the built executable file:
+Execute the built executable file:
```shell
./build/ppocr
+
+
+
+Among them, y' is the output of the activation function, and the value is between 0-1. It adds a modulation factor (1-y’)^γ and a balance factor α on the basis of the original cross-entropy loss. When α = 1, y = 1, the comparison between the loss function and the cross-entropy loss is shown in the following figure:
+
+
+
+
+
+
+
+
+As can be seen from the above figure, when γ > 0, the adjustment coefficient (1-y’)^γ gives smaller weight to the easy-to-classify sample loss, making the network pay more attention to the difficult and misclassified samples. The adjustment factor γ is used to adjust the rate at which the weight of simple samples decreases. When γ = 0, it is the cross-entropy loss function. When γ increases, the influence of the adjustment factor will also increase. Experiments revealed that 2 is the optimal value of γ. The balance factor α is used to balance the uneven proportions of the positive and negative samples. In the text, α is taken as 0.25.
+
+For the classic CTC algorithm, suppose a certain feature sequence (f1, f2, ......ft), after CTC decoding, the probability that the result is equal to label is y', then the probability that the CTC decoding result is not equal to label is (1-y'); it is not difficult to find that the CTCLoss value and y' have the following relationship:
+
+
+
+
+
+
+
+
+Combining the idea of Focal Loss, assigning larger weights to difficult samples and smaller weights to simple samples can make the network focus more on the mining of difficult samples and further improve the accuracy of recognition. Therefore, we propose Focal-CTC Loss. Its definition is as follows:
+
+
+
+
+
+
+
+
+In the experiment, the value of γ is 2, α = 1, see this for specific implementation: [rec_ctc_loss.py](../../ppocr/losses/rec_ctc_loss.py)
+
+
+
+## 2. A-CTC Loss
+
+A-CTC Loss is short for CTC Loss + ACE Loss. Among them, ACE Loss was proposed by the paper, “[Aggregation Cross-Entropy for Sequence Recognition](https://arxiv.org/abs/1904.08364)”. Compared with CTCLoss, ACE Loss has the following two advantages:
++ ACE Loss can solve the recognition problem of 2-D text, while CTCLoss can only process 1-D text
++ ACE Loss is better than CTC loss in time complexity and space complexity
+
+The advantages and disadvantages of the OCR recognition algorithm summarized by the predecessors are shown in the following figure:
+
+
+
+
+
+
+
+Although ACELoss does handle 2D predictions, as shown in the figure above, and has advantages in memory usage and inference speed, in practice, we found that using ACELoss alone, the recognition effect is not as good as CTCLoss. Consequently, we tried to combine CTCLoss and ACELoss, and CTCLoss is the mainstay while ACELoss acts as an auxiliary supervision loss. This attempt has achieved better results. On our internal experimental data set, compared to using CTCLoss alone, the recognition accuracy can be improved by about 1%.
+A_CTC Loss is defined as follows:
+
+
+
+
+
+
+
+
+In the experiment, λ = 0.1. See the ACE loss implementation code: [ace_loss.py](../../ppocr/losses/ace_loss.py)
+
+
+
+## 3. C-CTC Loss
+
+C-CTC Loss is short for CTC Loss + Center Loss. Among them, Center Loss was proposed by the paper, “[A Discriminative Feature Learning Approach for Deep Face Recognition](https://link.springer.com/chapter/10.1007/978-3-319-46478-7_31)“. It was first used in face recognition tasks to increase the distance between classes and reduce the distance within classes. It is an earlier and also widely used algorithm.
+
+In the task of Chinese OCR recognition, through the analysis of bad cases, we found that a major difficulty in Chinese recognition is that there are many similar characters, which are easy to misunderstand. From this, we thought about whether we can learn from the idea of n to increase the class spacing of similar characters, to improve recognition accuracy. However, Metric Learning is mainly used in the field of image recognition, and the label of the training data is a fixed value; for OCR recognition, it is a sequence recognition task essentially, and there is no explicit alignment between features and labels. Therefore, how to combine the two is still a direction worth exploring.
+
+By trying Arcmargin, Cosmargin and other methods, we finally found that Centerloss can help further improve the accuracy of recognition. C_CTC Loss is defined as follows:
+
+
+
+
+
+
+In the experiment, we set λ=0.25. See the center_loss implementation code: [center_loss.py](../../ppocr/losses/center_loss.py)
+
+It is worth mentioning that in C-CTC Loss, choosing to initialize the Center randomly does not bring significant improvement. Our Center initialization method is as follows:
++ Based on the original CTCLoss, a network N is obtained by training
++ Select the training set, identify the completely correct part, and form the set G
++ Send each sample in G to the network, perform forward calculation, and extract the correspondence between the input of the last FC layer (ie feature) and the result of argmax calculation (ie index)
++ Aggregate features with the same index, calculate the average, and get the initial center of each character.
+
+Taking the configuration file `configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml` as an example, the center extraction command is as follows:
+
+```
+python tools/export_center.py -c configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml -o Global.pretrained_model="./output/rec_mobile_pp-OCRv2/best_accuracy"
+```
+
+After running, `train_center.pkl` will be generated in the main directory of PaddleOCR.
+
+
+
+## 4. Experiment
+
+For the above three solutions, we conducted training and evaluation based on Baidu's internal data set. The experimental conditions are shown in the following table:
+
+| algorithm | Focal_CTC | A_CTC | C-CTC |
+| :-------- | :-------- | ----: | :---: |
+| gain | +0.3% | +0.7% | +1.7% |
+
+Based on the above experimental conclusions, we adopted the C-CTC strategy in PP-OCRv2. It is worth mentioning that, because PP-OCRv2 deals with the recognition task of 6625 Chinese characters, the character set is relatively large and there are many similar characters, so the C-CTC solution brings a significant improvement on this task. But if you switch to other OCR recognition tasks, the conclusion may be different. You can try Focal-CTC, A-CTC, C-CTC, and the combined solution EnhancedCTC. We believe it will bring different degrees of improvement.
+
+The unified combined plan is shown in the following file: [rec_enhanced_ctc_loss.py](../../ppocr/losses/rec_enhanced_ctc_loss.py)
\ No newline at end of file
diff --git a/doc/doc_en/environment_en.md b/doc/doc_en/environment_en.md
index 854db648fc255d36ec5c01c710a1d3cab53aaeb5..fc87f10c104628df0268bc6f8910c5914aeba225 100644
--- a/doc/doc_en/environment_en.md
+++ b/doc/doc_en/environment_en.md
@@ -4,9 +4,9 @@ Windows and Mac users are recommended to use Anaconda to build a Python environm
Recommended working environment:
- PaddlePaddle >= 2.0.0 (2.1.2)
-- python3.7
-- CUDA10.1 / CUDA10.2
-- CUDNN 7.6
+- Python 3.7
+- CUDA 10.1 / CUDA 10.2
+- cuDNN 7.6
* [1. Python Environment Setup](#1)
+ [1.1 Windows](#1.1)
@@ -25,7 +25,7 @@ Recommended working environment:
#### 1.1.1 Install Anaconda
-- Note: To use paddlepaddle you need to install python environment first, here we choose python integrated environment Anaconda toolkit
+- Note: To use PaddlePaddle you need to install python environment first, here we choose python integrated environment Anaconda toolkit
- Anaconda is a common python package manager
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment.
@@ -44,19 +44,19 @@ Recommended working environment:
+
- - Check conda to add environment variables and ignore the warning that
+ - Check Conda to add environment variables and ignore the warning that
-#### 1.1.2 Opening the terminal and creating the conda environment
+#### 1.1.2 Opening the terminal and creating the Conda environment
- Open Anaconda Prompt terminal: bottom left Windows Start Menu -> Anaconda3 -> Anaconda Prompt start console
-- Create a new conda environment
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment named paddle_env
@@ -70,7 +70,7 @@ Recommended working environment:
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -91,7 +91,7 @@ The above anaconda environment and python environment are installed
#### 1.2.1 Installing Anaconda
-- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
+- Note: To use PaddlePaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Anaconda is a common python package manager
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
@@ -108,17 +108,17 @@ The above anaconda environment and python environment are installed
- Just follow the default settings, it will take a while to install
-- It is recommended to install a code editor such as vscode or pycharm
+- It is recommended to install a code editor such as VSCode or PyCharm
-#### 1.2.2 Open a terminal and create a conda environment
+#### 1.2.2 Open a terminal and create a Conda environment
- Open the terminal
- Press command and spacebar at the same time, type "terminal" in the focus search, double click to enter terminal
-- **Add conda to the environment variables**
+- **Add Conda to the environment variables**
- - Environment variables are added so that the system can recognize the conda command
+ - Environment variables are added so that the system can recognize the Conda command
- Open `~/.bash_profile` in the terminal by typing the following command.
@@ -126,7 +126,7 @@ The above anaconda environment and python environment are installed
vim ~/.bash_profile
```
- - Add conda as an environment variable in `~/.bash_profile`.
+ - Add Conda as an environment variable in `~/.bash_profile`.
```shell
# Press i first to enter edit mode
@@ -156,12 +156,12 @@ The above anaconda environment and python environment are installed
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- - Verify that the conda command is recognized.
+ - Verify that the Conda command is recognized.
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
+ - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then Conda has been added to the environment variables
-- Create a new conda environment
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment called paddle_env
@@ -175,7 +175,7 @@ The above anaconda environment and python environment are installed
- 
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -198,7 +198,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
#### 1.3.1 Anaconda environment configuration
-- Note: To use paddlepaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
+- Note: To use PaddlePaddle you need to install the python environment first, here we choose the python integrated environment Anaconda toolkit
- Anaconda is a common python package manager
- After installing Anaconda, you can install the python environment, as well as numpy and other required toolkit environment
@@ -214,9 +214,9 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- Select the appropriate version for your operating system
- Type `uname -m` in the terminal to check the command set used by your system
- - Download method 1: Download locally, then transfer the installation package to the linux server
+ - Download method 1: Download locally, then transfer the installation package to the Linux server
- - Download method 2: Directly use linux command line to download
+ - Download method 2: Directly use Linux command line to download
```shell
# First install wget
@@ -277,12 +277,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
- When you are done, press `esc` to exit edit mode, then type `:wq!` and enter to save and exit
- - Verify that the conda command is recognized.
+ - Verify that the Conda command is recognized.
- Enter `source ~/.bash_profile` in the terminal to update the environment variables
- - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then conda has been added to the environment variables
+ - Enter `conda info --envs` in the terminal again, if it shows that there is a base environment, then Conda has been added to the environment variables
-- Create a new conda environment
+- Create a new Conda environment
```shell
# Enter the following command at the command line to create an environment called paddle_env
@@ -296,7 +296,7 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
-- To activate the conda environment you just created, enter the following command at the command line.
+- To activate the Conda environment you just created, enter the following command at the command line.
```shell
# Activate the paddle_env environment
@@ -335,13 +335,13 @@ sudo docker container exec -it ppocr /bin/bash
## 2. Install PaddlePaddle 2.0
-- If you have cuda9 or cuda10 installed on your machine, please run the following command to install
+- If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
```bash
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```
-- If you only have cpu on your machine, please run the following command to install
+- If you have no available GPU on your machine, please run the following command to install the CPU version
```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
index 019ac4d0ac15aceed89286048d2c4d88a259e501..a8a96e30f020b98b52bb465140b3463cd88beebb 100755
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -139,7 +139,7 @@ tar xf ch_ppocr_mobile_v2.0_det_infer.tar
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
```
-The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
+The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
@@ -244,7 +244,7 @@ The visualized text detection results are saved to the `./inference_results` fol
## 3. Text Recognition Model Inference
-The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
+The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inference. Please check below for details.
diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md
index 62a672885c86119ae56dc93ef76c2bb746084a05..21f4c64d6dc99054a3480a66cd710b5d09461ca1 100755
--- a/doc/doc_en/inference_ppocr_en.md
+++ b/doc/doc_en/inference_ppocr_en.md
@@ -7,7 +7,7 @@ This article introduces the use of the Python inference engine for the PP-OCR mo
- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
- [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
- - [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
+ - [2. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
@@ -25,7 +25,7 @@ tar xf ch_PP-OCRv2_det_infer.tar
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
```
-The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
+The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
@@ -75,7 +75,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
-### 2. Multilingaul Model Inference
+### 2. 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:
diff --git a/doc/doc_en/installation_en.md b/doc/doc_en/installation_en.md
index dec384b2f27f8bb36ee67d8b040b532b30e0b028..bb499f5cd4b70da59445bec4f74ca4e0f4e517b1 100644
--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
@@ -1,17 +1,17 @@
## QUICK INSTALLATION
-After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
+After testing, PaddleOCR can run on glibc 2.23. You can also test other glibc versions or install glibc 2.23 for the best compatibility.
PaddleOCR working environment:
- PaddlePaddle 2.0.0
-- python3.7
+- Python 3.7
- glibc 2.23
-It is recommended to use the docker provided by us to run PaddleOCR, please refer to the use of docker [link](https://www.runoob.com/docker/docker-tutorial.html/).
+It is recommended to use the docker provided by us to run PaddleOCR. Please refer to the docker tutorial [link](https://www.runoob.com/docker/docker-tutorial.html/).
-*If you want to directly run the prediction code on mac or windows, you can start from step 2.*
+*If you want to directly run the prediction code on Mac or Windows, you can start from step 2.*
-**1. (Recommended) Prepare a docker environment. The first time you use this docker image, it will be downloaded automatically. Please be patient.**
+**1. (Recommended) Prepare a docker environment. For the first time you use this docker image, it will be downloaded automatically. Please be patient.**
```
# Switch to the working directory
cd /home/Projects
@@ -22,7 +22,7 @@ cd /home/Projects
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
```
-If using CUDA10, please run the following command to create a container.
+With CUDA10, please run the following command to create a container.
It is recommended to set a shared memory greater than or equal to 32G through the --shm-size parameter:
```
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
@@ -51,11 +51,11 @@ For more software version requirements, please refer to the instructions in [Ins
# Recommend
git clone https://github.com/PaddlePaddle/PaddleOCR
-# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
+# If you cannot pull successfully due to network problems, you can switch to the mirror hosted on Gitee:
git clone https://gitee.com/paddlepaddle/PaddleOCR
-# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
+# Note: The mirror on Gitee may not keep in synchronization with the latest update with the project on GitHub. There might be a delay of 3-5 days. Please try GitHub at first.
```
**4. Install third-party libraries**
@@ -66,6 +66,6 @@ pip3 install -r requirements.txt
If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.
-Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
+Please try to download Shapely whl file from [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
diff --git a/doc/doc_en/models_en.md b/doc/doc_en/models_en.md
index 37c4a174563abc68085a103e11e2ddb3bd954714..d60ca2a3329f0c1b1ad0bfe6f200d0d5e63dcdaa 100644
--- a/doc/doc_en/models_en.md
+++ b/doc/doc_en/models_en.md
@@ -7,13 +7,13 @@ This section contains two parts. Firstly, [PP-OCR Model Download](./models_list_
Let's first understand some basic concepts.
-- [Introduction about OCR](#introduction-about-ocr)
+- [Introduction to OCR](#introduction-to-ocr)
* [Basic Concepts of OCR Detection Model](#basic-concepts-of-ocr-detection-model)
* [Basic Concepts of OCR Recognition Model](#basic-concepts-of-ocr-recognition-model)
* [PP-OCR Model](#pp-ocr-model)
-## 1. Introduction about OCR
+## 1. Introduction to OCR
This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model.
diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md
index e3cf251c3439ba009a1de0ba48f7c0aa10b117c4..b07d5f0a9471a1bdff7a93328d63e874fed60e19 100644
--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
@@ -1,6 +1,6 @@
# OCR Model List（V2.1, updated on 2021.9.6）
> **Note**
-> 1. Compared with the model v2.0, the 2.1 version of the detection model has a improvement in accuracy, and the 2.1 version of the recognition model is optimized in accuracy and CPU speed.
+> 1. Compared with the model v2.0, the 2.1 version of the detection model has a improvement in accuracy, and the 2.1 version of the recognition model has optimizations in accuracy and speed with CPU.
> 2. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
> 3. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
@@ -18,7 +18,7 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine
|--- | --- | --- |
|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)|
|trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
-|slim model|\*.nb| Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
+|slim model|\*.nb| Model compressed by PaddleSlim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
Relationship of the above models is as follows.
@@ -50,7 +50,7 @@ Relationship of the above models is as follows.
|ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
-**Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
+**Note:** The `trained model` is fine-tuned on the `pre-trained model` with real data and synthesized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthesized data, which is more suitable for fine-tune on your own dataset.
### 2.2 English Recognition Model
diff --git a/doc/doc_en/multi_languages_en.md b/doc/doc_en/multi_languages_en.md
index 545be5524f2c52c9799d3b013f1aac8baf1a379f..9f09b531d9f6f9912b69804e57cf4e78f0c15531 100644
--- a/doc/doc_en/multi_languages_en.md
+++ b/doc/doc_en/multi_languages_en.md
@@ -28,12 +28,12 @@ The multilingual models cover Latin, Arabic, Traditional Chinese, Korean, Japane
This document will briefly introduce how to use the multilingual model.
- [1 Installation](#Install)
- - [1.1 paddle installation](#paddleinstallation)
- - [1.2 paddleocr package installation](#paddleocr_package_install)
+ - [1.1 Paddle installation](#paddleinstallation)
+ - [1.2 PaddleOCR package installation](#paddleocr_package_install)
- [2 Quick Use](#Quick_Use)
- [2.1 Command line operation](#Command_line_operation)
- - [2.2 python script running](#python_Script_running)
+ - [2.2 Run with Python script](#python_Script_running)
- [3 Custom Training](#Custom_Training)
- [4 Inference and Deployment](#inference)
- [4 Supported languages and abbreviations](#language_abbreviations)
@@ -42,7 +42,7 @@ This document will briefly introduce how to use the multilingual model.
## 1 Installation
-### 1.1 paddle installation
+### 1.1 Paddle installation
```
# cpu
pip install paddlepaddle
@@ -52,7 +52,7 @@ pip install paddlepaddle-gpu
```
-### 1.2 paddleocr package installation
+### 1.2 PaddleOCR package installation
pip install
@@ -79,8 +79,8 @@ paddleocr -h
* Whole image prediction (detection + recognition)
-Paddleocr currently supports 80 languages, which can be switched by modifying the --lang parameter.
-The specific supported [language] (#language_abbreviations) can be viewed in the table.
+PaddleOCR currently supports 80 languages, which can be specified by the --lang parameter.
+The supported languages are listed in the [table](#language_abbreviations).
``` bash
paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
@@ -90,7 +90,7 @@ paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
@@ -18,11 +18,11 @@ PaddleOCR contains rich text detection, text recognition and end-to-end algorith
# Recommend
git clone https://github.com/PaddlePaddle/PaddleOCR
-# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
+# If you cannot pull successfully due to network problems, you can switch to the mirror hosted on Gitee:
git clone https://gitee.com/paddlepaddle/PaddleOCR
-# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
+# Note: The mirror on Gitee may not keep in synchronization with the latest project on GitHub. There might be a delay of 3-5 days. Please try GitHub at first.
```
### **2.2 Install third-party libraries**
@@ -34,6 +34,6 @@ pip3 install -r requirements.txt
If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows.
-Please try to download Shapely whl file using [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
+Please try to download Shapely whl file from [http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
-Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
\ No newline at end of file
+Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
diff --git a/doc/doc_en/pgnet_en.md b/doc/doc_en/pgnet_en.md
index e176a1260c734974e2dad843faeb3e5532176629..c7cb3221ccfd897e2fd9062a828c2fe0ceb42024 100644
--- a/doc/doc_en/pgnet_en.md
+++ b/doc/doc_en/pgnet_en.md
@@ -6,18 +6,18 @@
+