This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).
This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCRv3 models list](./models_list_en.md).
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Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline.
-[7.2 OCR and table recognition model](#72-ocr-and-table-recognition-model)
-[7.3 KIE model](#73-kie-model)
-[2. Features](#2-features)
-[3. Results](#3-results)
-[3.1 Layout analysis and table recognition](#31-layout-analysis-and-table-recognition)
-[3.2 Layout Recovery](#32-layout-recovery)
-[3.3 KIE](#33-kie)
-[4. Quick start](#4-quick-start)
-[5. Model List](#5-model-list)
## 1. Introduction
PP-Structure is an OCR toolkit that can be used for document analysis and processing with complex structures, designed to help developers better complete document understanding tasks
PP-Structure is an intelligent document analysis system developed by the PaddleOCR team, which aims to help developers better complete tasks related to document understanding such as layout analysis and table recognition.
## 2. Update log
* 2022.02.12 KIE add LayoutLMv2 model。
* 2021.12.07 add [KIE SER and RE tasks](kie/README.md)。
The pipeline of PP-Structurev2 system is shown below. The document image first passes through the image direction correction module to identify the direction of the entire image and complete the direction correction. Then, two tasks of layout information analysis and key information extraction can be completed.
## 3. Features
- In the layout analysis task, the image first goes through the layout analysis model to divide the image into different areas such as text, table, and figure, and then analyze these areas separately. For example, the table area is sent to the form recognition module for structured recognition, and the text area is sent to the OCR engine for text recognition. Finally, the layout recovery module restores it to a word or pdf file with the same layout as the original image;
- In the key information extraction task, the OCR engine is first used to extract the text content, and then the SER(semantic entity recognition) module obtains the semantic entities in the image, and finally the RE(relationship extraction) module obtains the correspondence between the semantic entities, thereby extracting the required key information.
More technical details: 👉 [PP-Structurev2 Technical Report](docs/PP-Structurev2_introduction.md)
- Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser)
- Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR)
- Support to extract excel files from the table areas
- Support python whl package and command line usage, easy to use
- Support custom training for layout analysis and table structure tasks
- Support Document Key Information Extraction (KIE) tasks: Semantic Entity Recognition (SER) and Relation Extraction (RE)
PP-Structurev2 supports independent use or flexible collocation of each module. For example, you can use layout analysis alone or table recognition alone. Click the corresponding link below to get the tutorial for each independent module:
The figure shows the pipeline of layout analysis + table recognition. The image is first divided into four areas of image, text, title and table by layout analysis, and then OCR detection and recognition is performed on the three areas of image, text and title, and the table is performed table recognition, where the image will also be stored for use.
Different colored boxes in the figure represent different categories. For xfun dataset, there are three categories: query, answer and header:
The main features of PP-Structurev2 are as follows:
- Support layout analysis of documents in the form of images/pdfs, which can be divided into areas such as **text, titles, tables, figures, formulas, etc.**;
- Support common Chinese and English **table detection** tasks;
- Support structured table recognition, and output the final result to **Excel file**;
- Support multimodal-based Key Information Extraction (KIE) tasks - **Semantic Entity Recognition** (SER) and **Relation Extraction (RE);
- Support **layout recovery**, that is, restore the document in word or pdf format with the same layout as the original image;
- Support customized training and multiple inference deployment methods such as python whl package quick start;
- Connect with the semi-automatic data labeling tool PPOCRLabel, which supports the labeling of layout analysis, table recognition, and SER.
* Dark purple: header
* Light purple: query
* Army green: answer
## 3. Results
The corresponding category and OCR recognition results are also marked at the top left of the OCR detection box.
PP-Structurev2 supports the independent use or flexible collocation of each module. For example, layout analysis can be used alone, or table recognition can be used alone. Only the visualization effects of several representative usage methods are shown here.
The figure shows the pipeline of layout analysis + table recognition. The image is first divided into four areas of image, text, title and table by layout analysis, and then OCR detection and recognition is performed on the three areas of image, text and title, and the table is performed table recognition, where the image will also be stored for use.
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.
The following figure shows the effect of layout recovery based on the results of layout analysis and table recognition in the previous section.
In PP-Structure, the image will be divided into 5 types of areas **text, title, image list and table**. For the first 4 types of areas, directly use PP-OCR system to complete the text detection and recognition. For the table area, after the table structuring process, the table in image is converted into an Excel file with the same table style.
Layout analysis classifies image by region, including the use of Python scripts of layout analysis tools, extraction of designated category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README.md).
Table recognition converts table images into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed instructions, please refer to [document](table/README.md)
In the figure, the red box represents `Question`, the blue box represents `Answer`, and `Question` and `Answer` are connected by green lines.
Multi-modal based Key Information Extraction (KIE) methods include Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks. 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. For details, please refer to [document](kie/README.md)
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet dataset can divide image into 5 types of areas **text, title, table, picture, and list** | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}|
Start from [Quick Start](./docs/quickstart_en.md).
|ch_ppstructure_mobile_v2.0_SLANet|Chinese table recognition model based on SLANet|9.3M|[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar) |
Some tasks need to use both the structured analysis models and the OCR models. For example, the table recognition task needs to use the table recognition model for structured analysis, and the OCR model to recognize the text in the table. Please select the appropriate models according to your specific needs.
### 7.3 KIE model
For structural analysis related model downloads, please refer to:
-[PP-Structure Model Zoo](./docs/models_list_en.md)
|model name|description|model size|download|
| --- | --- | --- | --- |
|ser_LayoutXLM_xfun_zhd|SER model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
|re_LayoutXLM_xfun_zh|RE model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
For OCR related model downloads, please refer to:
-[PP-OCR Model Zoo](../doc/doc_en/models_list_en.md)
If you need to use other models, you can download the model in [PPOCR model_list](../doc/doc_en/models_list_en.md) and [PPStructure model_list](./docs/models_list.md)
After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each table in the image will be stored as an excel. The filename of excel is their coordinates in the image.
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
### 1.2 Install PaddleOCR Whl Package
```bash
# Install paddleocr, version 2.6 is recommended
pip3 install"paddleocr>=2.6"
# Install the KIE dependency packages (if you do not use the KIE, you can skip it)
pip install-r kie/requirements.txt
# Install the image direction classification dependency package paddleclas (if you do not use the image direction classification, you can skip it)
pip3 install paddleclas
# Install the KIE dependency packages (if you do not use the KIE, you can skip it)
Please refer to: [Key Information Extraction](../kie/README.md).
Key information extraction does not currently support use by the whl package. For detailed usage tutorials, please refer to: [Key Information Extraction](../kie/README.md).
Please refer to: [Key Information Extraction](../kie/README.md).
Key information extraction does not currently support use by the whl package. For detailed usage tutorials, please refer to: [Key Information Extraction](../kie/README.md).
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#### 2.2.6 layout recovery
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@@ -197,7 +218,7 @@ from paddelocr.ppstructure.recovery.recovery_to_doc import sorted_layout_boxes,
Most of the parameters are consistent with the PaddleOCR whl package, see [whl package documentation](../../doc/doc_en/whl.md)
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## 3. Summary
Through the content in this section, you can master the use of PP-Structure related functions through PaddleOCR whl package. Please refer to [documentation tutorial](../../README.md) for more detailed usage tutorials including model training, inference and deployment, etc.
