[cherry-pick] Release/2.9, updata en docs (#14045)
* updata en docs * updata en docs * updata en docs * updata en docs * updata en docs * updata en docspull/14047/head
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@ -29,6 +29,14 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC
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## 📣 Recent updates ([more](https://paddlepaddle.github.io/PaddleOCR/latest/en/update.html))
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- **🔥 2024.10.18 release PaddleOCR v2.9, including**:
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* PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
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* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**.
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* 🚀 [**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 edge 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**.
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* Supports [PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md), [high-precision layout detection model based on RT-DETR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) and [high-efficiency layout area detection model based on PicoDet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md), [high-precision table structure recognition model](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md), text image unwarping model [UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md), formula recognition model [LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md), and [document image orientation classification model based on PP-LCNet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md).
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- **🔥2024.7 Added PaddleOCR Algorithm Model Challenge Champion Solutions**:
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- Challenge One, OCR End-to-End Recognition Task Champion Solution: [Scene Text Recognition Algorithm-SVTRv2](https://paddlepaddle.github.io/PaddleOCR/algorithm/text_recognition/algorithm_rec_svtrv2.html);
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- Challenge Two, General Table Recognition Task Champion Solution: [Table Recognition Algorithm-SLANet-LCNetV2](https://paddlepaddle.github.io/PaddleOCR/algorithm/table_recognition/algorithm_table_slanet.html).
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@ -31,6 +31,14 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC
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## 📣 Recent updates
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- **🔥 2024.10.18 release PaddleOCR v2.9, including**:
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* PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
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* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**.
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* 🚀 [**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 edge 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**.
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* Supports [PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md), [high-precision layout detection model based on RT-DETR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) and [high-efficiency layout area detection model based on PicoDet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md), [high-precision table structure recognition model](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md), text image unwarping model [UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md), formula recognition model [LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md), and [document image orientation classification model based on PP-LCNet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md).
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- **🔥2023.8.7 Release PaddleOCR[release/2.7](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.7)**
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- Release [PP-OCRv4](./ppocr/overview.en.md), support mobile version and server version
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@ -0,0 +1,136 @@
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## 1. Introduction to All-in-One Development
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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:
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* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 forcasting, forming 20+ single-function modules, making it convenient for developers to use **model combinations**.
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* 🚀 [**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 edge 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**.
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> **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.
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## 2. OCR-Related Capability Support
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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_deploy_en.md)/[service-oriented deployment](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/service_deploy_en.md)/[edge deployment](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/lite_deploy_en.md). 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.
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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).
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<table>
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<tr>
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<th>Pipeline</th>
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<th>Online Experience</th>
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<th>Local Inference</th>
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<th>High-Performance Inference</th>
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<th>Service-Oriented Deployment</th>
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<th>Edge Deployment</th>
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<th>Custom Development</th>
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<th><a href="https://aistudio.baidu.com/pipeline/mine">No-Code Development On AI Studio</a></td>
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</tr>
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<tr>
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<tr>
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<td>OCR</td>
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<td><a href="https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">Link</a></td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>PP-ChatOCRv3</td>
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<td><a href="https://aistudio.baidu.com/community/app/182491/webUI?source=appCenter">Link</a></td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>Table Recognition</td>
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<td><a href="https://aistudio.baidu.com/community/app/91661?source=appMineRecent">Link</a></td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>Layout Parsing</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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</tr>
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<tr>
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<td>Formula Recognition</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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</tr>
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<tr>
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<td>Seal Recognition</td>
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<td>🚧</td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>🚧</td>
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<td>✅</td>
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<td>🚧</td>
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</tr>
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</table>
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> ❗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.
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**🚀 Support for Domestic Hardware Capabilities**
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<table>
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<tr>
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<th>Pipeline Name</th>
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<th>Ascend 910B</th>
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<th>Kunlunxin XPU</th>
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<th>Cambricon MLU</th>
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<th>Haiguang DCU</th>
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</tr>
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<tr>
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<td>General OCR</td>
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<td>✅</td>
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<td>✅</td>
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<td>✅</td>
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<td>🚧</td>
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</tr>
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<tr>
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<td>Table Recognition</td>
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<td>✅</td>
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<td>🚧</td>
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<td>🚧</td>
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<td>🚧</td>
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</tr>
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</table>
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## 3. List and Tutorials of OCR-Related Model Pipelines
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- **OCR Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/OCR_en.md)
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- **Table Recognition Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_en.md)
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- **PP-ChatOCRv3-doc Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md)
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- **Layout Parsing Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_en.md)
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- **Formula Recognition Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition_en.md)
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- **Seal Recognition Pipeline**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition_en.md)
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## 4. List and Tutorials of OCR-Related Modules
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- **Text Detection Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_detection_en.md)
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- **Seal Detection Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/seal_text_detection_en.md)
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- **Text Recognition Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_recognition_en.md)
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- **Formula Recognition Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md)
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- **Table Structure Recognition Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md)
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- **Text Image Unwarping Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md)
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- **Layout Detection Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md)
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- **Document Image Orientation Classification Module**: [Tutorial](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md)
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# Quick Start
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> **Note:**
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>* 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 **all-in-one** development capabilities in the OCR field. Through all-in-one development, simple and efficient model use, combination, and customization can be achieved.
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>* 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. This document provides quick inference usage of the **OCR-related pipelines**. For quick usage of single-function modules and more features, please refer to the relevant sections in [All-in-One Development of PaddleOCR](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html).
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### 🛠️ Installation
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> ❗Before installing PaddleX, please ensure you have a basic **Python environment** (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).
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* **Installing PaddlePaddle**
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```bash
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# cpu
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python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
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# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
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python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
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# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
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python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
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```
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> ❗For more PaddlePaddle versions, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
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* **Installing PaddleX**
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```bash
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pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b1-py3-none-any.whl
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```
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> ❗For more installation methods, refer to the [PaddleX Installation Guide](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/installation/installation_en.md).
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### 📝 Python Script Usage
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A few lines of code can complete the quick inference of the pipeline, the unified Python script format is as follows:
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```python
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from paddlex import create_pipeline
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pipeline = create_pipeline(pipeline=[Pipeline Name])
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output = pipeline.predict([Input Image Name])
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for res in output:
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res.print()
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res.save_to_img("./output/")
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res.save_to_json("./output/")
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```
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The following steps are executed:
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* `create_pipeline()` instantiates the pipeline object
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* Passes the image and calls the `predict` method of the pipeline object for inference prediction
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* Processes the prediction results
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For other pipelines in Python scripts, just adjust the `pipeline` parameter of the `create_pipeline()` method to the corresponding name of the pipeline. Below is a list of each pipeline's corresponding parameter name and detailed usage explanation:
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<b>👉 More Python Script Usages for Pipelines</b>
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| Pipeline Name | Corresponding Parameter | Detailed Explanation |
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|------------------------|--------------------------|---------------------------------------------------------------------------------------------------------------------------|
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| PP-ChatOCRv3-doc | `PP-ChatOCRv3-doc` | [Python Script Usage for PP-ChatOCRv3-doc Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md#22-local-experience) |
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| General OCR | `OCR` | [Python Script Usage for General OCR Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/OCR_en.md#22-local-experience) |
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| Table Recognition | `table_recognition` | [Python Script Usage for Table Recognition Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_en.md#22-local-experience) |
|
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| Layout Parsing | `layout_parsing` | [Python Script Usage for Layout Parsing Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing_en.md#22-local-experience) |
|
||||
| Formula Recognition | `formula_recognition` | [Python Script Usage for Formula Recognition Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition_en.md#2-quick-start) |
|
||||
| Seal Recognition | `seal_recognition` | [Python Script Usage for Formula Recognition Pipeline](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition_en.md#2--quick-start) |
|
||||
|
||||
### 💻 Command Line Usage
|
||||
|
||||
You can quickly experience the pipeline effect with a single command. The unified command line format is:
|
||||
|
||||
```bash
|
||||
paddlex --pipeline [pipeline_name] --input [input_image] --device [device]
|
||||
```
|
||||
|
||||
You only need to specify three parameters:
|
||||
* `pipeline`: the name of the pipeline
|
||||
* `input`: the local path or URL of the input file (e.g., image) to be processed
|
||||
* `device`: the GPU index to use (e.g., `gpu:0` means using the first GPU), or you can choose to use the CPU (`cpu`)
|
||||
|
||||
For example, for the General OCR pipeline:
|
||||
```bash
|
||||
paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0
|
||||
```
|
||||
|
||||
The result of the operation is as follows:
|
||||
|
||||
```bash
|
||||
{'input_path': '/root/.paddlex/predict_input/general_ocr_002.png', 'dt_polys': [[[5, 12], [88, 10], [88, 29], [5, 31]], [[208, 14], [249, 14], [249, 22], [208, 22]], [[695, 15], [824, 15], [824, 60], [695, 60]], [[158, 27], [355, 23], [356, 70], [159, 73]], [[421, 25], [659, 19], [660, 59], [422, 64]], [[337, 104], [460, 102], [460, 127], [337, 129]], [[486, 103], [650, 100], [650, 125], [486, 128]], [[675, 98], [835, 94], [835, 119], [675, 124]], [[64, 114], [192, 110], [192, 131], [64, 134]], [[210, 108], [318, 106], [318, 128], [210, 130]], [[82, 140], [214, 138], [214, 163], [82, 165]], [[226, 136], [328, 136], [328, 161], [226, 161]], [[404, 134], [432, 134], [432, 161], [404, 161]], [[509, 131], [570, 131], [570, 158], [509, 158]], [[730, 138], [771, 138], [771, 154], [730, 154]], [[806, 136], [817, 136], [817, 146], [806, 146]], [[342, 175], [470, 173], [470, 197], [342, 199]], [[486, 173], [616, 171], [616, 196], [486, 198]], [[677, 169], [813, 166], [813, 191], [677, 194]], [[65, 181], [170, 177], [171, 202], [66, 205]], [[96, 208], [171, 205], [172, 230], [97, 232]], [[336, 220], [476, 215], [476, 237], [336, 242]], [[507, 217], [554, 217], [554, 236], [507, 236]], [[87, 229], [204, 227], [204, 251], [87, 254]], [[344, 240], [483, 236], [483, 258], [344, 262]], [[66, 252], [174, 249], [174, 271], [66, 273]], [[75, 279], [264, 272], [265, 297], [76, 303]], [[459, 297], [581, 295], [581, 320], [459, 322]], [[101, 314], [210, 311], [210, 337], [101, 339]], [[68, 344], [165, 340], [166, 365], [69, 368]], [[345, 350], [662, 346], [662, 368], [345, 371]], [[100, 459], [832, 444], [832, 465], [100, 480]]], 'dt_scores': [0.8183103704439653, 0.7609575621092027, 0.8662357274035412, 0.8619508290334809, 0.8495855993183273, 0.8676840017933314, 0.8807986687956436, 0.822308525056085, 0.8686617037621976, 0.8279022169854463, 0.952332847006758, 0.8742692553015098, 0.8477013022907575, 0.8528771493227294, 0.7622965906848765, 0.8492388224448705, 0.8344203789965632, 0.8078477124353284, 0.6300434587457232, 0.8359967356998494, 0.7618617265751318, 0.9481573079350023, 0.8712182945408912, 0.837416955846334, 0.8292475059403851, 0.7860382856406026, 0.7350527486717117, 0.8701022267947695, 0.87172526903969, 0.8779847108088126, 0.7020437651809734, 0.6611684983372949], 'rec_text': ['www.997', '151', 'PASS', '登机牌', 'BOARDING', '舱位 CLASS', '序号SERIALNO.', '座位号SEATNO', '航班 FLIGHT', '日期DATE', 'MU 2379', '03DEC', 'W', '035', 'F', '1', '始发地FROM', '登机口 GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO.', '姓名NAME', 'ZHANGQIWEI', '票号TKTNO.', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE1OMINUTESBEFOREDEPARTURETIME'], 'rec_score': [0.9617719054222107, 0.4199012815952301, 0.9652514457702637, 0.9978302121162415, 0.9853208661079407, 0.9445787072181702, 0.9714463949203491, 0.9841841459274292, 0.9564052224159241, 0.9959094524383545, 0.9386572241783142, 0.9825271368026733, 0.9356589317321777, 0.9985442161560059, 0.3965512812137604, 0.15236201882362366, 0.9976775050163269, 0.9547433257102966, 0.9974752068519592, 0.9646636843681335, 0.9907559156417847, 0.9895358681678772, 0.9374122023582458, 0.9909093379974365, 0.9796401262283325, 0.9899340271949768, 0.992210865020752, 0.9478569626808167, 0.9982215762138367, 0.9924325942993164, 0.9941263794898987, 0.96443772315979]}
|
||||
......
|
||||
```
|
||||
|
||||
The visualization result is as follows:
|
||||
|
||||
|
||||
|
||||
For command line usage of other pipelines, simply adjust the `pipeline` parameter to the name of the respective pipeline. Below are the commands corresponding to each pipeline:
|
||||
|
||||
<b>👉 More Command Line Usages for Pipelines</b>
|
||||
|
||||
| Pipeline Name | Command |
|
||||
|-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| Table Recognition | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0` |
|
||||
| Layout Parsing | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0` |
|
||||
| Formula Recognition | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --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 --device gpu:0`
|
||||
|
|
@ -60,9 +60,9 @@ for res in output:
|
|||
| 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://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction.md#22-本地体验) |
|
||||
| 通用OCR | `OCR` | [通用OCR产线Python脚本使用说明](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/OCR.md#222-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://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.md#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](./docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md#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](./docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.md#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](./docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md#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://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md#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://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.md#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://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md#22-python脚本方式集成) |
|
||||
|
||||
### 💻 命令行使用
|
||||
|
||||
|
|
|
|||
|
|
@ -6,8 +6,11 @@ hide:
|
|||
|
||||
- Web online experience
|
||||
- PP-OCRv4 online experience：<https://aistudio.baidu.com/aistudio/projectdetail/6611435>
|
||||
- PP-ChatOCR online experience：<https://aistudio.baidu.com/aistudio/projectdetail/6488689>
|
||||
- One line of code quick use: [Quick Start（Chinese/English/Multilingual/Document Analysis](./ppocr/quick_start.en.md)
|
||||
- Full-process experience of training, inference, and high-performance deployment in the Paddle AI suite (PaddleX)：
|
||||
- PP-OCRv4：<https://aistudio.baidu.com/aistudio/modelsdetail?modelId=286>
|
||||
- PP-ChatOCR：<https://aistudio.baidu.com/aistudio/modelsdetail?modelId=332>
|
||||
- SLANet online experience：<https://aistudio.baidu.com/community/app/91661>
|
||||
- PP-ChatOCRv3-doc online experience：<https://aistudio.baidu.com/community/app/182491>
|
||||
- PP-ChatOCRv2-common online experience：<https://aistudio.baidu.com/community/app/91662>
|
||||
- PP-ChatOCRv2-doc online experience：<https://aistudio.baidu.com/community/app/70303>
|
||||
|
||||
- [One-Click Call for 17 Core PaddleOCR Models](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html)
|
||||
- One line of code quick use: [Text Detection and Recognition (Chinese/English/Multilingual)](https://paddlepaddle.github.io/PaddleOCR/latest/en/ppocr/overview.html)
|
||||
- One line of code quick use: [Document Analysis](https://paddlepaddle.github.io/PaddleOCR/latest/en/ppstructure/overview.html)
|
||||
|
|
|
|||
|
|
@ -7,6 +7,14 @@ hide:
|
|||
|
||||
### Recently Update
|
||||
|
||||
- **🔥 2024.10.18 release PaddleOCR v2.9, including**:
|
||||
* PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
|
||||
* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 forcasting, forming 20+ 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 edge 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**.
|
||||
|
||||
* Supports [PP-ChatOCRv3-doc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md), [high-precision layout detection model based on RT-DETR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) and [high-efficiency layout area detection model based on PicoDet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection_en.md), [high-precision table structure recognition model](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md), text image unwarping model [UVDoc](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md), formula recognition model [LatexOCR](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md), and [document image orientation classification model based on PP-LCNet](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md).
|
||||
|
||||
#### 2022.5.9 release PaddleOCR v2.5, including
|
||||
|
||||
- [PP-OCRv3](./ppocr_introduction_en.md#pp-ocrv3): With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%.
|
||||
|
|
|
|||
|
|
@ -100,6 +100,9 @@ plugins:
|
|||
Home: Home
|
||||
快速开始: Quick Start
|
||||
近期更新: Recently Update
|
||||
低代码全流程开发: All-in-One Development
|
||||
概述: Overview
|
||||
快速开始: Quick Start
|
||||
模型: Model
|
||||
概述: Overview
|
||||
PP-OCR 文本检测识别: PP-OCR
|
||||
|
|
|
|||
Loading…
Reference in New Issue