- Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude French, German, Korean, and Japanese.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude French, German, Korean, and Japanese.
For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
-**Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/PPOCRLabel.py#L116) :
-**Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/PPOCRLabel.py#L116) :
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md).
Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Model Pruning is a technique that reduces this redundancy by removing the sub-models in the neural network model, so as to reduce model calculation complexity and improve model inference performance.
This example uses PaddleSlim provided[APIs of Pruning](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/) to compress the OCR model.
This example uses PaddleSlim provided[APIs of Pruning](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/) to compress the OCR model.
[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), an open source library which integrates model pruning, quantization (including quantization training and offline quantization), distillation, neural network architecture search, and many other commonly used and leading model compression technique in the industry.
It is recommended that you could understand following pages before reading this example：
...
...
@@ -37,25 +37,26 @@ PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.
After the pre-trained model is loaded, sensitivity analysis is performed on each network layer of the model to understand the redundancy of each network layer, and save a sensitivity file which named: sen.pickle. After that, user could load the sensitivity file via the [methods provided by PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L221) and determining the pruning ratio of each network layer automatically. For specific details of sensitivity analysis, see：[Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/image_classification_sensitivity_analysis_tutorial.md)
The function would return a dict after loading the sensitivity file. The keys of the dict are name of parameters in each layer. And the value of key is the information about pruning sensitivity of corresponding layer. In example, pruning 10% filter of the layer corresponding to conv10_expand_weights would lead to 0.65% degradation of model performance. The details could be seen at: [Sensitivity analysis](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/algo/algo.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)
Enter the PaddleOCR root directory，perform sensitivity analysis on the model with the following command：
Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment.
Recommended working environment:
- PaddlePaddle >= 2.0.0 (2.1.2)
- PaddlePaddle >= 2.1.2
- Python 3.7
- CUDA 10.1 / CUDA 10.2
- cuDNN 7.6
> If you already have a Python environment installed, you can skip to [PaddleOCR Quick Start](./quickstart_en.md).
*[1. Python Environment Setup](#1)
+[1.1 Windows](#1.1)
+[1.2 Mac](#1.2)
+[1.3 Linux](#1.3)
*[2. Install PaddlePaddle 2.0](#2)
<aname="1"></a>
...
...
@@ -330,21 +331,3 @@ You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags
# ctrl+P+Q to exit docker, to re-enter docker using the following command:
sudo docker container exec -it ppocr /bin/bash
```
<aname="2"></a>
## 2. Install PaddlePaddle 2.0
- If you have CUDA 9 or CUDA 10 installed on your machine, please run the following command to install
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model.
This chapter contains three parts. Firstly, [PP-OCR Model Download](./models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](./config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](./inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
------
Let's first understand some basic concepts.
-[INTRODUCTION ABOUT OCR](#introduction-about-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)
*[And a table of contents](#and-a-table-of-contents)
*[On the right](#on-the-right)
## 1. INTRODUCTION ABOUT OCR
This section briefly introduces the basic concepts of OCR detection model and recognition model, and introduces PaddleOCR's PP-OCR model.
OCR (Optical Character Recognition, Optical Character Recognition) is currently the general term for text recognition. It is not limited to document or book text recognition, but also includes recognizing text in natural scenes. It can also be called STR (Scene Text Recognition).
OCR text recognition generally includes two parts, text detection and text recognition. The text detection module first uses detection algorithms to detect text lines in the image. And then the recognition algorithm to identify the specific text in the text line.
### 1.1 BASIC CONCEPTS OF OCR DETECTION MODEL
Text detection can locate the text area in the image, and then usually mark the word or text line in the form of a bounding box. Traditional text detection algorithms mostly extract features manually, which are characterized by fast speed and good effect in simple scenes, but the effect will be greatly reduced when faced with natural scenes. Currently, deep learning methods are mostly used.
Text detection algorithms based on deep learning can be roughly divided into the following categories:
1. Method based on target detection. Generally, after the text box is predicted, the final text box is filtered through NMS, which is mostly four-point text box, which is not ideal for curved text scenes. Typical algorithms are methods such as EAST and Text Box.
2. Method based on text segmentation. The text line is regarded as the segmentation target, and then the external text box is constructed through the segmentation result, which can handle curved text, and the effect is not ideal for the text cross scene problem. Typical algorithms are DB, PSENet and other methods.
3. Hybrid target detection and segmentation method.
### 1.2 Basic concepts of OCR recognition model
The input of the OCR recognition algorithm is generally text lines images which has less background information, and the text information occupies the main part. The recognition algorithm can be divided into two types of algorithms:
1. CTC-based method. The text prediction module of the recognition algorithm is based on CTC, and the commonly used algorithm combination is CNN+RNN+CTC. There are also some algorithms that try to add transformer modules to the network and so on.
2. Attention-based method. The text prediction module of the recognition algorithm is based on Attention, and the commonly used algorithm combination is CNN+RNN+Attention.
### 1.3 PP-OCR model
PaddleOCR integrates many OCR algorithms, text detection algorithms include DB, EAST, SAST, etc., text recognition algorithms include CRNN, RARE, StarNet, Rosetta, SRN and other algorithms.
Among them, PaddleOCR has released the PP-OCR series model for the general OCR in Chinese and English natural scenes. The PP-OCR model is composed of the DB+CRNN algorithm. It uses massive Chinese data training and model tuning methods to have high text detection and recognition capabilities in Chinese scenes. And PaddleOCR has launched a high-precision and ultra-lightweight PP-OCRv2 model. The detection model is only 3M, and the recognition model is only 8.5M. Using [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)'s model quantification method, the detection model can be compressed to 0.8M without reducing the accuracy. The recognition is compressed to 3M, which is more suitable for mobile deployment scenarios.
The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
The `points` in the dictionary represent the multi-point coordinates (such as: 4 points, 8 points and 14 points, etc.) of the text box, arranged clockwise from the point at the upper left corner.
`transcription` represents the text of the current text box. **When its content is "###" it means that the text box is invalid and will be skipped during training.**
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
<aname="12-install-paddleocr-whl-package"></a>
### 1.2 Install PaddleOCR Whl Package
```bash
pip install"paddleocr>=2.0.1"# Recommend to use version 2.0.1+
In this section, you have mastered the use of PaddleOCR whl packages and obtained results.
PaddleOCR is a rich and practical OCR tool library that opens up the whole process of data, model training, compression and inference deployment, so in the [next section](./paddleOCR_overview_en.md) we will first introduce you to the overview of PaddleOCR, and then clone the PaddleOCR project to start the application journey of PaddleOCR.
This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR.
[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
*[1. 快速使用](#1-----)
*[2. 执行训练](#2-----)
*[3. 执行评估](#3-----)
*[1. Quick Use](#1-----)
*[2. Model Training](#2-----)
*[3. Model Evaluation](#3-----)
<aname="1-----"></a>
## 1. 快速使用
训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集：
## 1. Quick Use
```
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
The prediction result is saved as `./output/sdmgr_kie/predicts_kie.txt`, and the visualization results are saved in the folder`/output/sdmgr_kie/kie_results/`.
可视化结果如下图所示：
The visualization results are shown in the figure below:
<divalign="center">
<imgsrc="./imgs/0.png"width="800">
</div>
<aname="2-----"></a>
## 2. 执行训练
## 2. Model Training
创建数据集软链到PaddleOCR/train_data目录下：
```
Create a softlink to the folder, `PaddleOCR/train_data`:
The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. The default training data path in the configuration file is `train_data/wildreceipt`. After preparing the data, you can execute the model training with the following command:
This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR.
本节介绍PaddleOCR中关键信息提取SDMGR方法的快速使用和训练方法。
[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
```shell
```
wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
```
Download the pretrained model and predict the result:
执行预测：
```shell
```
cd PaddleOCR/
wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
The prediction result is saved as `./output/sdmgr_kie/predicts_kie.txt`, and the visualization results are saved in the folder`/output/sdmgr_kie/kie_results/`.
The visualization results are shown in the figure below:
可视化结果如下图所示：
<divalign="center">
<imgsrc="./imgs/0.png"width="800">
</div>
<aname="2-----"></a>
## 2. Model Training
## 2. 执行训练
Create a softlink to the folder, `PaddleOCR/train_data`:
```shell
创建数据集软链到PaddleOCR/train_data目录下：
```
cd PaddleOCR/ && mkdir train_data && cd train_data
ln -s ../../wildreceipt ./
```
The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. The default training data path in the configuration file is `train_data/wildreceipt`. After preparing the data, you can execute the model training with the following command:
Document Visual Q&A, mainly for the image content of the question and answer, DOC-VQA is a type of VQA task, DOC-VQA mainly asks questions about the textual content of text images.
- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)。
- 支持SER任务和RE任务的自定义训练。
- 支持OCR+SER的端到端系统预测与评估。
- 支持OCR+SER+RE的端到端系统预测。
- Integrated LayoutXLM model and PP-OCR prediction engine.
- Support Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks based on multi-modal methods. Based on SER task, text recognition and classification in images can be completed. Based on THE RE task, we can extract the relation of the text content in the image, such as judge the problem pair.
**Note**: This project is based on the open source implementation of [LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf) on Paddle 2.2, and at the same time, after in-depth polishing by the flying Paddle team and the Industrial and **Commercial Bank of China** in the scene of real estate certificate, jointly open source.
| 模型 | 任务 | f1 | 模型下载地址 |
## 1.Performance
We evaluated the algorithm on [XFUN](https://github.com/doc-analysis/XFUND) 's Chinese data set, and the performance is as follows
| Model | Task | F1 | Model Download Link |
|:---:|:---:|:---:| :---:|
| LayoutXLM | RE | 0.7113 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
| LayoutXLM | SER | 0.9056 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
| LayoutLM | SER | 0.78 | [链接](https://paddleocr.bj.bcebos.com/pplayout/LayoutLM_ser_pretrained.tar) |
| LayoutXLM | RE | 0.7113 | [Link](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
| LayoutXLM | SER | 0.9056 | [Link](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
| LayoutLM | SER | 0.78 | [Link](https://paddleocr.bj.bcebos.com/pplayout/LayoutLM_ser_pretrained.tar) |
## 2.效果演示
## 2.Demonstration
**注意：** 测试图片来源于XFUN数据集。
**Note**: the test images are from the xfun dataset.
In the figure, the red box represents the question, the blue box represents the answer, and the question and answer are connected by green lines. The corresponding category and OCR recognition results are also marked at the top left of the OCR detection box.
# Note: the code cloud hosting code may not be able to synchronize the update of this GitHub project in real time, with a delay of 3 ~ 5 days. Please give priority to the recommended method.
```
-**(3) Install PaddleNLP**
```bash
# You need to use the latest code version of paddlenlp for installation
Download address of processed xfun Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
下载并解压该数据集，解压后将数据集放置在当前目录下。
Download and unzip the dataset, and then place the dataset in the current directory.
If you want to convert data sets in other languages in xfun, you can refer to [xfun data conversion script.](helper/trans_xfun_data.py))
如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
If you want to experience the prediction process directly, you can download the pre training model provided by us, skip the training process and predict directly.
It will end up in output_res The visual image of the prediction result and the text file of the prediction result are saved in the res directory. The file name is infer_ results.txt.
The visual image of the prediction result and the text file of the prediction result are saved in the output_res file folder, the file name is`infer_results.txt`。
Document Visual Q&A, mainly for the image content of the question and answer, DOC-VQA is a type of VQA task, DOC-VQA mainly asks questions about the textual content of text images.
- Integrated LayoutXLM model and PP-OCR prediction engine.
- Support Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks based on multi-modal methods. Based on SER task, text recognition and classification in images can be completed. Based on THE RE task, we can extract the relation of the text content in the image, such as judge the problem pair.
- Support OCR+SER end-to-end system prediction and evaluation.
- Support OCR+SER+RE end-to-end system prediction.
## 1.性能
**Note**: This project is based on the open source implementation of [LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf) on Paddle 2.2, and at the same time, after in-depth polishing by the flying Paddle team and the Industrial and **Commercial Bank of China** in the scene of real estate certificate, jointly open source.
The corresponding category and OCR recognition results are also marked at the top left of the OCR detection box.
在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
### 2.2 RE
...
...
@@ -54,89 +51,79 @@ The corresponding category and OCR recognition results are also marked at the to
---|---
In the figure, the red box represents the question, the blue box represents the answer, and the question and answer are connected by green lines. The corresponding category and OCR recognition results are also marked at the top left of the OCR detection box.
# Note: the code cloud hosting code may not be able to synchronize the update of this GitHub project in real time, with a delay of 3 ~ 5 days. Please give priority to the recommended method.
```
-**(3) Install PaddleNLP**
```bash
# You need to use the latest code version of paddlenlp for installation
Download address of processed xfun Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
If you want to experience the prediction process directly, you can download the pre training model provided by us, skip the training process and predict directly.
如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
### 4.2 SER Task
### 4.2 SER任务
*Start training
*启动训练
```shell
python3.7 train_ser.py \
python3 train_ser.py \
--model_name_or_path"layoutxlm-base-uncased"\
--ser_model_type"LayoutXLM"\
--train_data_dir"XFUND/zh_train/image"\
...
...
@@ -152,12 +139,12 @@ python3.7 train_ser.py \
--seed 2048
```
Finally, Precision, Recall, F1 and other indicators will be printed, and the model and training log will be saved in/ In the output/Ser/ folder.
It will end up in output_res The visual image of the prediction result and the text file of the prediction result are saved in the res directory. The file name is infer_ results.txt.
Finally, Precision, Recall, F1 and other indicators will be printed
最终会打印出`precision`, `recall`, `f1`等指标
*The OCR recognition results provided in the evaluation set are used for prediction
*使用评估集合中提供的OCR识别结果进行预测
```shell
export CUDA_VISIBLE_DEVICES=0
...
...
@@ -309,13 +296,13 @@ python3 infer_re.py \
--seed 2048
```
The visual image of the prediction result and the text file of the prediction result are saved in the output_res file folder, the file name is`infer_results.txt`。
