PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
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- Static graph: develop branch
- Static graph: develop branch
**Recent updates**
**Recent updates**
- 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md);release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including:English, Chinese, German, French, Japanese,Spanish,Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including:English, Chinese, German, French, Japanese,Spanish,Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md),easy to synthesize a large number of images which are similar to the target scene image.
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md),easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
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PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
- 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 the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (combined 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 the texts from the text, title, picture and list areas (combined with PP-OCR)
- Support to extract excel files from the table areas
- Support to extract excel files from the table areas
- Support python whl package and command line usage, easy to use
- Support to use with python whl package and command line easily
- Support custom training for layout analysis and table structure tasks
- Support custom training for layout analysis and table structure tasks
- The total model size is only about 18.6M (continuous optimization)