update front page readme (#7309)

README.md

@@ -26,17 +26,16 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 </div>
 
 ## Recent updates
+- **🔥2022.8.24 Release PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)**
+  - Release [PP-Structurev2](./ppstructure/)，with functions and performance fully upgraded, adapted to Chinese scenes, and new support for [Layout Recovery](./ppstructure/recovery) and [PDF to Word]();
+  - [Layout Analysis](./ppstructure/layout) optimization: model storage reduced by 95%, while speed increased by 11 times, and the average CPU time-cost is only 41ms;
+  - [Table Recognition](./ppstructure/table) optimization: 3 optimization strategies are designed, and the model accuracy is improved by 6% under comparable time consumption;
+  - [Key Information Extraction](./ppstructure/kie) optimization：a visual-independent model structure is designed, the accuracy of semantic entity recognition is increased by 2.8%, and the accuracy of relation extraction is increased by 9.1%.
+  
 - **🔥2022.5.9 Release PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
     - Release [PP-OCRv3](./doc/doc_en/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%.
     - Release [PPOCRLabelv2](./PPOCRLabel): Add the annotation function for table recognition task, key information extraction task and irregular text image.
     - Release interactive e-book [*"Dive into OCR"*](./doc/doc_en/ocr_book_en.md), covers the cutting-edge theory and code practice of OCR full stack technology.
-- 2021.12.21 Release PaddleOCR [release/2.4](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4)
-    - Release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR).
-    - Release 1 key information extraction algorithm (SDMGR, [tutorial](./ppstructure/docs/kie_en.md)) and 3 [DocVQA](./ppstructure/vqa) algorithms (LayoutLM, LayoutLMv2, LayoutXLM).
-- 2021.9.7 Release PaddleOCR [release/2.3](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.3)
-    - Release [PP-OCRv2](./doc/doc_en/ppocr_introduction_en.md#pp-ocrv2). The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
-- 2021.8.3 Release PaddleOCR [release/2.2](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.2)
-    - Release a new structured documents analysis toolkit, i.e., [PP-Structure](./ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
 
 - [more](./doc/doc_en/update_en.md)
 
@@ -45,7 +44,9 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 
 PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution [PP-OCR](./doc/doc_en/ppocr_introduction_en.md) and [PP-Structure](./ppstructure/README.md) on this basis, and get through the whole process of data production, model training, compression, inference and deployment.
 
-![](./doc/features_en.png)
+<div align="center">
+    <img src="https://user-images.githubusercontent.com/25809855/186171245-40abc4d7-904f-4949-ade1-250f86ed3a90.png">
+</div>
 
 > It is recommended to start with the “quick experience” in the document tutorial
 

README_ch.md

@@ -27,21 +27,20 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 ## 近期更新
 
-- **🔥2022.7 发布[OCR场景应用集合](./applications)**
-  - 发布OCR场景应用集合，包含数码管、液晶屏、车牌、高精度SVTR模型等**7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。
+- **🔥2022.8.24 发布 PaddleOCR [release/2.6](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.6)**
+  - 发布[PP-Structurev2](./ppstructure/)，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)和[PDF转Word]()；
+  - [版面分析](./ppstructure/layout)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；
+  - [表格识别](./ppstructure/table)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%；
+  - [关键信息抽取](./ppstructure/kie)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%。
   
-- **🔥2022.5.9 发布PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
+- **🔥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。
+  
+- **2022.5.9 发布 PaddleOCR [release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)**
     - 发布[PP-OCRv3](./doc/doc_ch/ppocr_introduction.md#pp-ocrv3)，速度可比情况下，中文场景效果相比于PP-OCRv2再提升5%，英文场景提升11%，80语种多语言模型平均识别准确率提升5%以上；
     - 发布半自动标注工具[PPOCRLabelv2](./PPOCRLabel)：新增表格文字图像、图像关键信息抽取任务和不规则文字图像的标注功能；
     - 发布OCR产业落地工具集：打通22种训练部署软硬件环境与方式，覆盖企业90%的训练部署环境需求；
     - 发布交互式OCR开源电子书[《动手学OCR》](./doc/doc_ch/ocr_book.md)，覆盖OCR全栈技术的前沿理论与代码实践，并配套教学视频。
-- 2021.12.21 发布PaddleOCR [release/2.4](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4)
-    - OCR算法新增1种文本检测算法（[PSENet](./doc/doc_ch/algorithm_det_psenet.md)），3种文本识别算法（[NRTR](./doc/doc_ch/algorithm_rec_nrtr.md)、[SEED](./doc/doc_ch/algorithm_rec_seed.md)、[SAR](./doc/doc_ch/algorithm_rec_sar.md)）；
-    - 文档结构化算法新增1种关键信息提取算法（[SDMGR](./ppstructure/docs/kie.md)），3种[DocVQA](./ppstructure/vqa)算法（LayoutLM、LayoutLMv2，LayoutXLM）。
-- 2021.9.7 发布PaddleOCR [release/2.3](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.3)
-    - 发布[PP-OCRv2](./doc/doc_ch/ppocr_introduction.md#pp-ocrv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
-- 2021.8.3 发布PaddleOCR [release/2.2](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.2)
-    - 发布文档结构分析[PP-Structure](./ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
 
 > [更多](./doc/doc_ch/update.md)
 
@@ -49,7 +48,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 支持多种OCR相关前沿算法，在此基础上打造产业级特色模型[PP-OCR](./doc/doc_ch/ppocr_introduction.md)和[PP-Structure](./ppstructure/README_ch.md)，并打通数据生产、模型训练、压缩、预测部署全流程。
 
-![](./doc/features.png)
+<div align="center">
+    <img src="https://user-images.githubusercontent.com/25809855/186170862-b8f80f6c-fee7-4b26-badc-de9c327c76ce.png">
+</div>
 
 > 上述内容的使用方法建议从文档教程中的快速开始体验