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Correct some spellings & links in documents. #5205
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Thanks for your contribution! |
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十分感谢你的帮助,为PaddleOCR的文档找出了诸多错误。其中有部分修改可能需要重新提交,如果有疑问或需要沟通,请加入微信群后@本账号。期待与你联系!
README_ch.md
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@@ -80,7 +80,8 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库,助力 | |||
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- [运行环境准备](./doc/doc_ch/environment.md) | |||
- [快速开始(中英文/多语言/文档分析)](./doc/doc_ch/quickstart.md) | |||
- [PaddleOCR全景图与项目克隆](./doc/doc_ch/paddleOCR_overview.md) | |||
- [PaddleOCR全景图](./doc/doc_ch/paddleOCR_overview.md) | |||
- [快速安装](./doc/doc_ch/installation.md) |
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你好,installation.md已经废弃,保留原始写法即可
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已按审查意见更新。
This section will introduce how to configure the C++ environment and complete it in the Linux\Windows (CPU\GPU) environment | ||
PaddleOCR model deployment. | ||
This chapter introduces the C++ deployment steps of the PaddleOCR model. The corresponding Python predictive deployment method refers to [document](../../doc/doc_ch/inference.md). | ||
C++ is better than python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used. |
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此处请 @LDOUBLEV review
doc/doc_ch/paddleOCR_overview.md
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# PaddleOCR全景图与项目克隆 | |||
# PaddleOCR全景图 |
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此文件可以不进行更改
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已按审查意见还原此处修改。
doc/doc_en/distributed_training.md
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## Introduction | |||
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The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, ppocrv2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in paddleocr to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html). | |||
The high performance of distributed training is one of the core advantages of PaddlePaddle. In the classification task, distributed training can achieve almost linear speedup ratio. Generally, OCR training task need massive training data. Such as recognition, PPOCR v2.0 model is trained based on 1800W dataset, which is very time-consuming if using single machine. Therefore, the distributed training is used in PaddleOCR to speedup the training task. For more information about distributed training, please refer to [distributed training quick start tutorial](https://fleet-x.readthedocs.io/en/latest/paddle_fleet_rst/parameter_server/ps_quick_start.html). |
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此处的标准写法应该是PP-OCR
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已按审查意见更新。
doc/doc_en/paddleOCR_overview_en.md
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# PaddleOCR Overview and Project Clone |
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此文件可以不进行修改
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此文件已经还原,但它的翻译仍然有需要改进的地方,因此我还原后又进行了部分改进。
README.md
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@@ -102,7 +102,8 @@ For a new language request, please refer to [Guideline for new language_requests | |||
## Tutorials | |||
- [Environment Preparation](./doc/doc_en/environment_en.md) | |||
- [Quick Start](./doc/doc_en/quickstart_en.md) | |||
- [PaddleOCR Overview and Project Clone](./doc/doc_en/paddleOCR_overview_en.md) |
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此处可以保留原始写法
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已按审查意见还原此处更改。
README.md
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- Ultra lightweight PP-OCRv2 series models: detection (3.1M) + direction classifier (1.4M) + recognition 8.5M) = 13.0M | ||
- Ultra lightweight PP-OCR mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M | ||
- General PP-OCR server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M | ||
- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition | ||
- Support multi-language recognition: about 80 languages like Korean, Japanese, German, French, etc | ||
- Supports Chinese, English, and digit recognition, vertical text recognition, and long text recognition |
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这五条都作为单独特性,可以作为单独一句话,下方相同,可以保留support
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已按审查意见更改。
I've updated and force-pushed the branch according to the review comments above. |
A new change was added in paddleocr.py. |
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LGTM, 特别鸣谢
同时,上述修改请cherry-pick到 release2.4分支上,您就可以在Contributor中看到自己了,再次感谢 |
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