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@ -26,12 +26,10 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
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</div>
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## 📣 Recent updates
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- 💥 **Live Preview: Oct 24 - Oct 26, China Standard Time, 20:30**, Engineers@PaddleOCR will show PP-StructureV2 optimization strategy for 3 days.
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- Scan the QR code below using WeChat, follow the PaddlePaddle official account and fill out the questionnaire to join the WeChat group, get the live link and 20G OCR learning materials (including PDF2Word application, 10 models in vertical scenarios, etc.)
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- 🔨**2022.11 Add implementation of [4 cutting-edge algorithms](doc/doc_ch/algorithm_overview.md)**：Text Detection [DRRG](doc/doc_en/algorithm_det_drrg_en.md), Text Recognition [RFL](./doc/doc_en/algorithm_rec_rfl_en.md), Image Super-Resolution [Text Telescope](doc/doc_en/algorithm_sr_telescope_en.md)，Handwrittem Mathematical Expression Recognition [CAN](doc/doc_en/algorithm_rec_can_en.md)
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- **2022.10 release [optimized JS version PP-OCRv3 model](./deploy/paddlejs/README.md)** with 4.3M model size, 8x faster inference time, and a ready-to-use web demo
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- 💥 **Live Playback: Introduction to PP-StructureV2 optimization strategy **. Scan [the QR code below](#Community) using WeChat, follow the PaddlePaddle official account and fill out the questionnaire to join the WeChat group, get the live link and 20G OCR learning materials (including PDF2Word application, 10 models in vertical scenarios, etc.)
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<div align="center">
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<img src="https://user-images.githubusercontent.com/50011306/196944258-0eb82df1-d730-4b96-a350-c1d370fdc2b1.jpg" width = "150" height = "150" />
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</div>
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- **🔥2022.8.24 Release PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)**
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- Release [PP-StructureV2](./ppstructure/)，with functions and performance fully upgraded, adapted to Chinese scenes, and new support for [Layout Recovery](./ppstructure/recovery) and **one line command to convert PDF to Word**;
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@ -74,6 +72,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel
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- [Dive Into OCR ](./doc/doc_en/ocr_book_en.md)
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<a name="Community"></a>
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## 👫 Community
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- For international developers, we regard [PaddleOCR Discussions](https://github.com/PaddlePaddle/PaddleOCR/discussions) as our international community platform. All ideas and questions can be discussed here in English.
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28
README_ch.md
28
README_ch.md
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@ -27,27 +27,17 @@ 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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## 📣 近期更新
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- **💥 直播预告：10.24-10.26日每晚8点半**，PaddleOCR研发团队详解PP-StructureV2优化策略。微信扫描下方二维码，关注公众号并填写问卷后进入官方交流群，获取直播链接与20G重磅OCR学习大礼包（内含PDF转Word应用程序、10种垂类模型、《动手学OCR》电子书等）
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- 🔨**2022.11 新增实现[4种前沿算法](doc/doc_ch/algorithm_overview.md)**：文本检测 [DRRG](doc/doc_ch/algorithm_det_drrg.md), 文本识别 [RFL](doc/doc_ch/algorithm_rec_rfl.md), 文本超分[Text Telescope](doc/doc_ch/algorithm_sr_telescope.md)，公式识别[CAN](doc/doc_ch/algorithm_rec_can.md.md)
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- **2022.10 优化[JS版PP-OCRv3模型](./deploy/paddlejs/README_ch.md)**：模型大小仅4.3M，预测速度提升8倍，配套web demo开箱即用
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- **💥 直播回放：PaddleOCR研发团队详解PP-StructureV2优化策略**。微信扫描[下方二维码](#开源社区)，关注公众号并填写问卷后进入官方交流群，获取直播回放链接与20G重磅OCR学习大礼包（内含PDF转Word应用程序、10种垂类模型、《动手学OCR》电子书等）
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<div align="center">
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<img src="https://user-images.githubusercontent.com/50011306/196944258-0eb82df1-d730-4b96-a350-c1d370fdc2b1.jpg" width = "150" height = "150" />
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</div>
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- **🔥2022.8.24 发布 PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)**
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- 发布[PP-StructureV2](./ppstructure/README_ch.md)，系统功能性能全面升级，适配中文场景，新增支持[版面复原](./ppstructure/recovery/README_ch.md)，支持**一行命令完成PDF转Word**；
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- [版面分析](./ppstructure/layout/README_ch.md)模型优化：模型存储减少95%，速度提升11倍，平均CPU耗时仅需41ms；
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- [表格识别](./ppstructure/table/README_ch.md)模型优化：设计3大优化策略，预测耗时不变情况下，模型精度提升6%；
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- [关键信息抽取](./ppstructure/kie/README_ch.md)模型优化：设计视觉无关模型结构，语义实体识别精度提升2.8%，关系抽取精度提升9.1%。
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- **🔥2022.8 发布 [OCR场景应用集合](./applications)**
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- 包含数码管、液晶屏、车牌、高精度SVTR模型、手写体识别等**9个垂类模型**，覆盖通用，制造、金融、交通行业的主要OCR垂类应用。
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- **2022.8 新增实现[8种前沿算法](doc/doc_ch/algorithm_overview.md)**
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- 文本检测：[FCENet](doc/doc_ch/algorithm_det_fcenet.md), [DB++](doc/doc_ch/algorithm_det_db.md)
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- 文本识别：[ViTSTR](doc/doc_ch/algorithm_rec_vitstr.md), [ABINet](doc/doc_ch/algorithm_rec_abinet.md), [VisionLAN](doc/doc_ch/algorithm_rec_visionlan.md), [SPIN](doc/doc_ch/algorithm_rec_spin.md), [RobustScanner](doc/doc_ch/algorithm_rec_robustscanner.md)
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- 表格识别：[TableMaster](doc/doc_ch/algorithm_table_master.md)
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- **2022.8 发布 [OCR场景应用集合](./applications)**：包含数码管、液晶屏、车牌、高精度SVTR模型、手写体识别等**9个垂类模型**，覆盖通用，制造、金融、交通行业的主要OCR垂类应用。
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- **2022.5.9 发布 PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
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@ -79,23 +69,23 @@ 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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## 📚《动手学OCR》电子书
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- [《动手学OCR》电子书](./doc/doc_ch/ocr_book.md)
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<a name="开源社区"></a>
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## 👫 开源社区
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- **📑项目合作：** 如果您是企业开发者且有明确的OCR垂类应用需求，填写[问卷](https://paddle.wjx.cn/vj/QwF7GKw.aspx)后可免费与官方团队展开不同层次的合作。
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- **👫加入社区：** 微信扫描二维码并填写问卷之后，加入交流群领取20G重磅OCR学习大礼包
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- **包括《动手学OCR》电子书** ，配套讲解视频和notebook项目；PaddleOCR历次发版直播课视频；
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- **👫加入社区：** **微信扫描二维码并填写问卷之后，加入交流群领取20G重磅OCR学习大礼包**
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- **包括《动手学OCR》电子书** ，配套讲解视频和notebook项目；**PaddleOCR历次发版直播课回放链接**；
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- **OCR场景应用模型集合：** 包含数码管、液晶屏、车牌、高精度SVTR模型、手写体识别等垂类模型，覆盖通用，制造、金融、交通行业的主要OCR垂类应用。
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- PDF2Word应用程序；OCR社区优秀开发者项目分享视频。
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- **🏅️社区项目**：[社区项目](./doc/doc_ch/thirdparty.md)文档中包含了社区用户**使用PaddleOCR开发的各种工具、应用**以及**为PaddleOCR贡献的功能、优化的文档与代码**等，是官方为社区开发者打造的荣誉墙，也是帮助优质项目宣传的广播站。
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- **🎁社区常规赛**：社区常规赛是面向OCR开发者的积分赛事，覆盖文档、代码、模型和应用四大类型，以季度为单位评选并发放奖励，赛题详情与报名方法可参考[链接](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。
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<div align="center">
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<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG" width = "150" height = "150" />
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<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG" width = "150" height = "150",caption='' />
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<p>PaddleOCR官方交流群二维码</p>
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</div>
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<a name="模型下载"></a>
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## 🛠️ PP-OCR系列模型列表（更新中）
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@ -27,7 +27,7 @@ Paper:
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Referring to the [FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/scene-text-telescope) data download instructions, the effect of the super-score algorithm on the TextZoom test set is as follows:
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|Model|Backbone|config|Acc|Download link|
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|Text Gestalt|tsrn|21.56|0.7411| [configs/sr/sr_telescope.yml](../../configs/sr/sr_telescope.yml)|[train model](https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar)|
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The [TextZoom dataset](https://paddleocr.bj.bcebos.com/dataset/TextZoom.tar) comes from two superfraction data sets, RealSR and SR-RAW, both of which contain LR-HR pairs. TextZoom has 17367 pairs of training data and 4373 pairs of test data.
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