diff --git a/README.md b/README.md index a3f6c0a6057b5453f7b58a3c37ab05ea6f9da87d..ab53e165ee811489391a2ef8362323f369b085c7 100644 --- a/README.md +++ b/README.md @@ -152,7 +152,7 @@ For a new language request, please refer to [Guideline for new language_requests [1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). -[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon). +[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (https://arxiv.org/abs/2109.03144). diff --git a/doc/doc_ch/thirdparty.md b/doc/doc_ch/thirdparty.md index 7d9d820890c92021b1b040b4576232e544dfcb00..794a6a3c572acf3365d3415bca8a55fcac1124cc 100644 --- a/doc/doc_ch/thirdparty.md +++ b/doc/doc_ch/thirdparty.md @@ -24,6 +24,7 @@ PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实 | 通用工具 | [ocr_sdk](https://github.com/mymagicpower/AIAS/blob/main/1_image_sdks/text_recognition/ocr_sdk) | OCR java SDK工具箱 | [Calvin](https://github.com/mymagicpower) | | 通用工具 | [iocr](https://github.com/mymagicpower/AIAS/blob/main/8_suite_hub/iocr) | IOCR 自定义模板识别(支持表格识别) | [Calvin](https://github.com/mymagicpower) | | 通用工具 | [Lmdb Dataset Format Conversion Tool](https://github.com/OneYearIsEnough/PaddleOCR-Recog-LmdbDataset-Conversion) | 文本识别任务中lmdb数据格式转换工具 | [OneYearIsEnough](https://github.com/OneYearIsEnough) | +| 通用工具 | [用paddleocr打造一款“盗幕笔记”](https://github.com/kjf4096/paddleocr_dmbj) | 用PaddleOCR记笔记 | [kjf4096](https://github.com/kjf4096) | | 垂类工具 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/1054614?channelType=0&channel=0) | 英文视频自动生成字幕 | [叶月水狐](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/322052) | | 垂类工具 | [id_card_ocr](https://github.com/baseli/id_card_ocr) | 身份证复印件识别 | [baseli](https://github.com/baseli) | | 垂类工具 | [Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader) | 能看懂表格图片的数据助手 | [thunder95](https://github.com/thunder95]) | @@ -39,6 +40,7 @@ PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实 | 应用部署 | [PaddleOCR-Paddlejs-Vue-Demo](https://github.com/Lovely-Pig/PaddleOCR-Paddlejs-Vue-Demo) | 使用Paddle.js和Vue部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) | | 应用部署 | [PaddleOCR-Paddlejs-React-Demo](https://github.com/Lovely-Pig/PaddleOCR-Paddlejs-React-Demo) | 使用Paddle.js和React部署PaddleOCR | [Lovely-Pig](https://github.com/Lovely-Pig) | | 学术前沿模型训练与推理 | [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3397137) | StarNet-MobileNetV3算法–中文训练 | [xiaoyangyang2](https://github.com/xiaoyangyang2) | +| 学术前沿模型训练与推理 | [ABINet-paddle](https://github.com/Huntersdeng/abinet-paddle) | ABINet算法前向运算的paddle实现以及模型各部分的实现细节分析 | [Huntersdeng](https://github.com/Huntersdeng) | ### 1.2 为PaddleOCR新增功能 @@ -55,7 +57,7 @@ PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实 ### 1.4 文档优化与翻译 -- 非常感谢 **[RangeKing](https://github.com/RangeKing)** 贡献翻译《动手学OCR》notebook[电子书英文版](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/notebook/notebook_en)。 +- 非常感谢 **[RangeKing](https://github.com/RangeKing)，[HustBestCat](https://github.com/HustBestCat)** 贡献翻译《动手学OCR》notebook[电子书英文版](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/notebook/notebook_en)。 - 非常感谢 [thunderstudying](https://github.com/thunderstudying)，[RangeKing](https://github.com/RangeKing)，[livingbody](https://github.com/livingbody)， [WZMIAOMIAO](https://github.com/WZMIAOMIAO)，[haigang1975](https://github.com/haigang1975) 补充多个英文markdown文档。 - 非常感谢 **[fanruinet](https://github.com/fanruinet)** 润色和修复35篇英文文档([#5205](https://github.com/PaddlePaddle/PaddleOCR/pull/5205))。 - 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档。 diff --git a/doc/doc_en/models_and_config_en.md b/doc/doc_en/models_and_config_en.md index 414d844d63d51a2b53feea035c1f735594d73fe0..c47fb5597eb56c823dff4c6d52cf3b114f3d9c0e 100644 --- a/doc/doc_en/models_and_config_en.md +++ b/doc/doc_en/models_and_config_en.md @@ -1,7 +1,7 @@ # PP-OCR Model and Configuration The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model. -This chapter contains three parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](. /config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine. +This chapter contains three parts. Firstly, [PP-OCR Model Download](./models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](./config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](./inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine. ------