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## 1. Introduction to All-in-One Development
The All-in-One development tool [PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1), based on the advanced technology of PaddleOCR, supports **low-code full-process** development capabilities in the OCR field. Through low-code development, simple and efficient model use, combination, and customization can be achieved. This will significantly **reduce the time consumption** of model development, **lower its development difficulty**, and greatly accelerate the application and promotion speed of models in the industry. Features include:
* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **48 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 10 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 30+ single-function modules, making it convenient for developers to use **model combinations**.
* 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and on-device deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**.
> **Note**: PaddleX is committed to achieving pipeline-level model training, inference, and deployment. A model pipeline refers to a series of predefined development processes for specific AI tasks, including combinations of single models (single-function modules) that can independently complete a type of task.
## 2. OCR-Related Capability Support
In PaddleX, all 6 OCR-related pipelines support **local inference**, and some pipelines support **online experience**. You can quickly experience the pre-trained model effects of each pipeline. If you are satisfied with the pre-trained model effects of a pipeline, you can directly proceed with [high-performance inference](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/high_performance_inference_en.md)/[service-oriented deployment](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/service_deploy_en.md)/[on-device deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/on_device_deployment.html). If not satisfied, you can also use the **custom development** capabilities of the pipeline to improve the effects. For the complete pipeline development process, please refer to [PaddleX Pipeline Usage Overview](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/pipeline_develop_guide_en.md) or the tutorials for each pipeline.
In addition, PaddleX provides developers with a full-process efficient model training and deployment tool based on a [cloud-based GUI](https://aistudio.baidu.com/pipeline/mine). Developers **do not need code development**, just need to prepare a dataset that meets the pipeline requirements to **quickly start model training**. For details, please refer to the tutorial ["Developing Industrial-level AI Models with Zero Barrier"](https://aistudio.baidu.com/practical/introduce/546656605663301).
<table>
<tr>
<th>Pipeline</th>
<th>Online Experience</th>
<th>Local Inference</th>
<th>High-Performance Inference</th>
<th>Service-Oriented Deployment</th>
<th>On-Device Deployment</th>
<th>Custom Development</th>
<th><a href="https://aistudio.baidu.com/pipeline/mine">No-Code Development On AI Studio</a></td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">Document Image Preprocessing</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html">OCR</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">Link</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">Table Recognition</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/91661?source=appMineRecent">Link</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">Table Recognition V2</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">Link</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">Seal Recognition</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">Link</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">Layout Parsing</a></td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">Layout Parsing v2</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>🚧</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">PP-ChatOCRv3-doc</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/182491/webUI?source=appCenter">Link</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">PP-ChatOCRv4-doc</a></td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
</table>
</table>
> ❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Haiguang. The table below details the support status of the pipelines. For specific supported model lists, please refer to the [Model List (Kunlunxin XPU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_xpu_en.md)/[Model List (Ascend NPU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_npu_en.md)/[Model List (Cambricon MLU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_mlu_en.md)/[Model List (Haiguang DCU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_dcu_en.md). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.
**🚀 Support for Domestic Hardware Capabilities**
<table>
<tr>
<th>Pipeline Name</th>
<th>Ascend 910B</th>
<th>Kunlunxin XPU</th>
<th>Cambricon MLU</th>
<th>Haiguang DCU</th>
</tr>
<tr>
<td>General OCR</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
</tr>
<tr>
<td>Table Recognition</td>
<td>✅</td>
<td>🚧</td>
<td>🚧</td>
<td>🚧</td>
</tr>
</table>
## 3. List and Tutorials of OCR-Related Model Pipelines
- **OCR Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html)
- **Table Recognition Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html)
- **PP-ChatOCRv3-doc Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html)
- **Layout Parsing Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html)
- **Formula Recognition Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)
- **Seal Recognition Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
## 4. List and Tutorials of OCR-Related Modules
- **Text Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_detection.html)
- **Seal Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/seal_text_detection.html)
- **Text Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_recognition.html)
- **Formula Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/formula_recognition.html)
- **Table Structure Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_structure_recognition.html)
- **Text Image Unwarping Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
- **Layout Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html)
- **Document Image Orientation Classification Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
## 3. List of OCR-related Pipeline Models and Tutorials
- **Document Image Preprocessing Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html)
- **OCR Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html)
- **Table Recognition Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html)
- **Table Recognition v2 Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html)
- **Layout Parsing Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html)
- **Layout Parsing v2 Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html)
- **Formula Recognition**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)
- **Seal Recognition**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
- **PP-ChatOCRv3-doc Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html)
- **PP-ChatOCRv4-doc Pipeline**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html)
## 4. List of OCR-related Single Function Modules and Tutorials
- **Text Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_detection.html)
- **Seal Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/seal_text_detection.html)
- **Textline Orientation Classification Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/textline_orientation_classification.html)
- **Text Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_recognition.html)
- **Formula Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/formula_recognition.html)
- **Table Structure Recognition Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_structure_recognition.html)
- **Text Image Unwarping Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
- **Layout Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html)
- **Document Image Orientation Classification Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
- **Table Cells Detection Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_cells_detection.html)
- **Table Classification Module**: [Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_classification.html)

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comments: true
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## 1. 低代码全流程开发简介
飞桨低代码开发工具 [PaddleX](https://github.com/PaddlePaddle/PaddleX)，依托于 PaddleOCR 的先进技术，支持了 OCR 领域的**低代码全流程**开发能力。通过低代码开发，可实现简单且高效的模型使用、组合与定制。这将显著**减少模型开发的时间消耗**，**降低其开发难度**，大大加快模型在行业中的应用和推广速度。特色如下：
* 🎨 **模型丰富一键调用**：将文本图像智能分析、通用 OCR、通用版面解析、通用表格识别、公式识别、印章文本识别涉及的**48个模型**整合为 10 条模型产线，通过极简的**Python API一键调用**，快速体验模型效果。此外，同一套API，也支持图像分类、目标检测、图像分割、时序预测等共计**200+模型**，形成30+单功能模块，方便开发者进行**模型组合**使用。
* 🚀 **提高效率降低门槛**：提供基于**统一命令**和**图形界面**两种方式，实现模型简洁高效的使用、组合与定制。支持**高性能推理、服务化部署和端侧部署**等多种部署方式。此外，对于各种主流硬件如**英伟达GPU、昆仑芯、昇腾、寒武纪和海光**等，进行模型开发时，都可以**无缝切换**。
>**说明**：PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。
## 2. OCR相关能力支持
PaddleX 中 OCR 相关的 10 条产线均支持本地**快速推理**，部分产线支持**在线体验**，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能推理](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)/[服务化部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/service_deploy.html)/[端侧部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/on_device_deployment.html)，如果不满意，您也可以使用产线的**二次开发**能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/pipeline_develop_guide.html)或各产线使用教程。
此外，PaddleX为开发者提供了基于[云端图形化开发界面](https://aistudio.baidu.com/pipeline/mine)的全流程开发工具, 详细请参考[教程《零门槛开发产业级AI模型》](https://aistudio.baidu.com/practical/introduce/546656605663301)
<table >
<tr>
<th>模型产线</th>
<th>在线体验</th>
<th>快速推理</th>
<th>高性能推理</th>
<th>服务化部署</th>
<th>端侧部署</th>
<th>二次开发</th>
<th><a href = "https://aistudio.baidu.com/pipeline/mine">星河零代码产线</a></td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html">文档图像预处理</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">链接</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">通用表格识别</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/91661?source=appMineRecent">链接</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html">通用表格识别v2</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/387976/webUI?source=appCenter">链接</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">印章文本识别</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/387977/webUI?source=appCenter">链接</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">通用版面解析</a></td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html">通用版面解析v2</a></td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
<td>🚧</td>
<td>🚧</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3</a></td>
<td><a href = "https://aistudio.baidu.com/community/app/182491/webUI?source=appCenter">链接</a></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v4</a></td>
<td>🚧</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>🚧</td>
<td>✅</td>
<td>🚧</td>
</tr>
</table>
> ❗注：以上功能均基于GPU/CPU实现。PaddleX还可在昆仑芯、昇腾、寒武纪和海光等主流硬件上进行快速推理和二次开发。下表详细列出了模型产线的支持情况，具体支持的模型列表请参阅 [模型列表(NPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_npu.html) // [模型列表(XPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_xpu.html) // [模型列表(MLU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_mlu.html) // [模型列表DCU](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_dcu.html)。同时我们也在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。
**🚀 国产化硬件能力支持**
<table>
<tr>
<th>模型产线</th>
<th>昇腾 910B</th>
<th>昆仑芯 R200/R300</th>
<th>寒武纪 MLU370X8</th>
<th>海光 Z100/K100AI</th>
</tr>
<tr>
<td>通用OCR</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td>通用表格识别</td>
<td>✅</td>
<td>🚧</td>
<td>🚧</td>
<td>🚧</td>
</tr>
</table>
## 3. OCR相关模型产线列表和教程
* **文档图像预处理产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html)
* **通用OCR产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html)
* **通用表格识别产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html)
* **通用表格识别v2产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html)
* **通用版面解析产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html)
* **通用版面解析v2产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html)
* **公式识别**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)
* **印章文本识别**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
* **文档场景信息抽取v3产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html)
* **文档场景信息抽取v4产线**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html)
## 4. OCR相关单功能模块列表和教程
* **文本检测模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_detection.html)
* **印章文本检测模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/seal_text_detection.html)
* **文本行方向分类模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/textline_orientation_classification.html)
* **文本识别模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_recognition.html)
* **公式识别模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/formula_recognition.html)
* **表格结构识别模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_structure_recognition.html)
* **文本图像矫正模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
* **版面区域检测模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/layout_detection.html)
* **文档图像方向分类模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
* **表格单元格检测模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_cells_detection.html)
* **表格分类模块**: [使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_classification.html)

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# ⏭️ Quick Start
## 🛠️ Installation
> ❗Before installing PaddleX, please ensure you have a basic **Python runtime environment** (Note: Currently supports running under Python 3.8 to Python 3.10, with more Python versions under adaptation). The PaddlePaddle version required by PaddleX
* **Installing PaddlePaddle**
```bash
# CPU
python -m pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
# gpu，requires GPU driver version ≥450.80.02 (Linux) or ≥452.39 (Windows)
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# gpu，requires GPU driver version ≥545.23.06 (Linux) or ≥545.84 (Windows)
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
```
> ❗No need to focus on the CUDA version on the physical machine, only the GPU driver version needs attention. For more information on PaddlePaddle Wheel versions, please refer to the [PaddlePaddle Official Website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/en/install/pip/linux-pip.html).
* **Installing PaddleX**
```bash
pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl
```
> ❗For more installation methods, refer to the [PaddleX Installation Guide](https://paddlepaddle.github.io/PaddleX/latest/en/installation/installation.html).
## 💻 CLI Usage
One command can quickly experience the pipeline effect, the unified CLI format is:
```bash
paddlex --pipeline [Pipeline Name] --input [Input Image] --device [Running Device]
```
Each Pipeline in PaddleX corresponds to specific parameters, which you can view in the respective Pipeline documentation for detailed explanations. Each Pipeline requires specifying three necessary parameters:
* `pipeline`: The name of the Pipeline or the configuration file of the Pipeline
* `input`: The local path, directory, or URL of the input file (e.g., an image) to be processed
* `device`: The hardware device and its index to use (e.g., `gpu:0` indicates using the 0th GPU), or you can choose to use NPU (`npu:0`), XPU (`xpu:0`), CPU (`cpu`), etc.
For example, using the OCR pipeline:
```bash
paddlex --pipeline OCR \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
<summary><b>👉 Click to view the running result</b></summary>
```bash
{'res': {'input_path': 'general_ocr_002.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426, ...], 'rec_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[ 3, 10, 82, 33], ...], dtype=int16)}}
```
The visualization result is as follows:
![alt text](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png)
To use the command line for other pipelines, simply adjust the `pipeline` parameter to the name of the corresponding pipeline and modify the parameters accordingly. Below are the commands for each pipeline:
<summary><b>👉 More CLI usage for pipelines</b></summary>
| Pipeline Name | Command |
|------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| OCR | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
| Document Image Preprocessor | `paddlex --pipeline doc_preprocessor --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg --use_doc_orientation_classify True --use_doc_unwarping True --save_path ./output --device gpu:0` |
| Table Recognition | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0` |
| Table Recognition v2 | `paddlex --pipeline table_recognition_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0` |
| Formula Recognition | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --use_layout_detection True --use_doc_orientation_classify False --use_doc_unwarping False --layout_threshold 0.5 --layout_nms True --layout_unclip_ratio 1.0 --layout_merge_bboxes_mode large --save_path ./output --device gpu:0` |
| Seal Recognition | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --use_doc_orientation_classify False --use_doc_unwarping False --device gpu:0 --save_path ./output` |
| Layout Parsing | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
| Layout Parsing v2 | `paddlex --pipeline layout_parsing_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_v2_demo.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
## 📝 Python Script Usage
A few lines of code can complete the quick inference of the pipeline, the unified Python script format is as follows:
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline=[Pipeline Name])
output = pipeline.predict([Input Image Name])
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
The following steps are executed:
* `create_pipeline()` instantiates the pipeline object
* Passes the image and calls the `predict()` method of the pipeline object for inference prediction
* Processes the prediction results
To use the Python script for other pipelines, simply adjust the `pipeline` parameter in the `create_pipeline()` method to the name of the corresponding pipeline and modify the parameters accordingly. Below are the parameter names and detailed usage explanations for each pipeline:
👉 More Python script usage for pipelines
| pipeline Name | Corresponding Parameter | Detailed Explanation |
|-------------------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------|
| OCR | `OCR` | [Instructions for Using the General OCR Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html#222-python-script-integration) |
| Document Image Preprocessing | `doc_preprocessor` | [Instructions for Using the Document Image Preprocessing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html#212-python-script-integration) |
| Table Recognition | `table_recognition` | [Instructions for Using the General Table Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python-script-integration) |
| Table Recognition v2 | `table_recognition_v2` | [Instructions for Using the General Table Recognition v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html#22-python-script-integration) |
| Formula Recognition | `formula_recognition` | [Instructions for Using the Formula Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python-script-integration) |
| Seal Recognition | `seal_recognition` | [Instructions for Using the Seal Text Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python-script-integration) |
| Layout Parsing | `layout_parsing` | [Instructions for Using the General Layout Parsing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python-script-integration) |
| Layout Parsing v2 | `layout_parsing_v2` | [Instructions for Using the General Layout Parsing v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html#22-python-script-integration) |
| PP-ChatOCRv3-doc | `PP-ChatOCRv3-doc` | [PP-ChatOCRv3-doc Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html) |
| PP-ChatOCRv4-doc | `PP-ChatOCRv4-doc` | [PP-ChatOCRv4-doc Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html) |

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# 快速开始
>**说明：**
>* 飞桨低代码开发工具[PaddleX](https://github.com/PaddlePaddle/PaddleX)，依托于PaddleOCR的先进技术，支持了OCR领域的**低代码全流程**开发能力。通过低代码开发，可实现简单且高效的模型使用、组合与定制。
>* PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。本文档提供**OCR相关产线**的快速推理使用，单功能模块的快速使用以及更多功能请参考[PaddleOCR低代码全流程开发](https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/overview.html)中相关章节。
### 🛠️ 安装
> ❗在安装 PaddleX 之前，请确保您已具备基本的 **Python 运行环境**（注：目前支持 Python 3.8 至 Python 3.12）。PaddleX 3.0-rc0 版本依赖的 PaddlePaddle 版本为 3.0.0rc0。
* **安装 PaddlePaddle**
```bash
# CPU 版本
python -m pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
# GPU 版本，需显卡驱动程序版本 ≥450.80.02（Linux）或 ≥452.39（Windows）
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# GPU 版本，需显卡驱动程序版本 ≥545.23.06（Linux）或 ≥545.84（Windows）
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
```
> ❗无需关注物理机上的 CUDA 版本，只需关注显卡驱动程序版本。更多飞桨 Wheel 版本信息，请参考[飞桨官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html)。
* **安装PaddleX**
```bash
pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl
```
> ❗ 更多安装方式参考 [PaddleX 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)
### 💻 命令行使用
一行命令即可快速体验产线效果，统一的命令行格式为：
```bash
paddlex --pipeline [产线名称] --input [输入图片] --device [运行设备]
```
PaddleX的每一条产线对应特定的参数，您可以在各自的产线文档中查看具体的参数说明。每条产线需指定必要的三个参数：
* `pipeline`：产线名称或产线配置文件
* `input`：待处理的输入文件（如图片）的本地路径、目录或 URL
* `device`：使用的硬件设备及序号（例如`gpu:0`表示使用第 0 块 GPU），也可选择使用 NPU(`npu:0`)、 XPU(`xpu:0`)、CPU(`cpu`)等。
以通用 OCR 产线为例：
```bash
paddlex --pipeline OCR \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
<b>👉 点击查看运行结果 </b><
```bash
{'res': {'input_path': 'general_ocr_002.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426, ...], 'rec_polys': [array([[ 3, 10],
[82, 10],
[82, 33],
[ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[ 3, 10, 82, 33], ...], dtype=int16)}}
```
可视化结果如下：
![alt text](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png)
其他产线的命令行使用，只需将 `pipeline` 参数调整为相应产线的名称，参数调整为对应的产线的参数即可。下面列出了每个产线对应的命令：
<b>👉 更多产线的命令行使用</b>
| 产线名称 | 使用命令 |
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 文档图像预处理 | `paddlex --pipeline doc_preprocessor --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg --use_doc_orientation_classify True --use_doc_unwarping True --save_path ./output --device gpu:0` |
| 通用OCR | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
| 通用表格识别 | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0` |
| 通用表格识别v2 | `paddlex --pipeline table_recognition_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0` |
| 公式识别 | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --use_layout_detection True --use_doc_orientation_classify False --use_doc_unwarping False --layout_threshold 0.5 --layout_nms True --layout_unclip_ratio 1.0 --layout_merge_bboxes_mode large --save_path ./output --device gpu:0` |
| 印章文本识别 | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --use_doc_orientation_classify False --use_doc_unwarping False --device gpu:0 --save_path ./output` |
| 通用版面解析 | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
| 通用版面解析v2 | `paddlex --pipeline layout_parsing_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_v2_demo.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` |
### 📝 Python 脚本使用
几行代码即可完成产线的快速推理，统一的 Python 脚本格式如下：
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline=[产线名称])
output = pipeline.predict([输入图片名称])
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
执行了如下几个步骤：
* `create_pipeline()` 实例化产线对象
* 传入图片并调用产线对象的 `predict()` 方法进行推理预测
* 对预测结果进行处理
其他产线的 Python 脚本使用，只需将 `create_pipeline()` 方法的 `pipeline` 参数调整为相应产线的名称，参数调整为对应的产线的参数即可。下面列出了每个产线对应的参数名称及详细的使用解释：
<b>👉 更多产线的Python脚本使用</b>
| 产线名称 | 对应参数 | 详细说明 |
|--------------------|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 通用OCR | `OCR` | [通用OCR产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html#222-python脚本方式集成) |
| 文档图像预处理 | `doc_preprocessor` | [文档图像预处理产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html#212-python脚本方式集成) |
| 通用表格识别 | `table_recognition` | [通用表格识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python脚本方式集成) |
| 通用表格识别v2 | `table_recognition_v2` | [通用表格识别v2产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html#22-python脚本方式集成) |
| 公式识别 | `formula_recognition` | [公式识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成) |
| 印章文本识别 | `seal_recognition` | [印章文本识别产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成) |
| 通用版面解析 | `layout_parsing` | [通用版面解析产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python脚本方式集成) |
| 通用版面解析v2 | `layout_parsing_v2` | [通用版面解析v2产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.html#22-python脚本方式集成) |
| 文档场景信息抽取v3 | `PP-ChatOCRv3-doc` | [文档场景信息抽取v3产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html#22-本地体验) |
| 文档场景信息抽取v4 | `PP-ChatOCRv4-doc` | [文档场景信息抽取v4产线Python脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html#22-本地体验) |