[Docs] update 1.x info (#1369)

* update 1.x info

* Update README_zh-CN.md

Co-authored-by: Tong Gao <[email protected]>

README.md

@@ -71,6 +71,8 @@ The main branch works with **PyTorch 1.6+**.
 
 ## What's New
 
+### 💎 Stable version
+
 v0.6.1 was released in 2022-08-04.
 
 1. ArT dataset is available for text detection and recognition!
@@ -79,6 +81,22 @@ v0.6.1 was released in 2022-08-04.
 
 Read [Changelog](https://mmocr.readthedocs.io/en/latest/changelog.html) for more details!
 
+### 🌟 Preview of 1.x version
+
+A brand new version of **MMOCR v1.0.0rc0** was released in 2022-09-01:
+
+1. **New engines**. MMOCR 1.x is based on [MMEngine](https://github.com/open-mmlab/mmengine), which provides a general and powerful runner that allows more flexible customizations and significantly simplifies the entrypoints of high-level interfaces.
+
+2. **Unified interfaces**. As a part of the OpenMMLab 2.0 projects, MMOCR 1.x unifies and refactors the interfaces and internal logics of train, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logics to allow the emergence of multi-task/modality algorithms.
+
+3. **Cross project calling**. Benefiting from the unified design, you can use the models implemented in other OpenMMLab projects, such as MMDet. We provide an example of how to use MMDetection's Mask R-CNN through `MMDetWrapper`. Check our documents for more details. More wrappers will be released in the future.
+
+4. **Stronger visualization**. We provide a series of useful tools which are mostly based on brand-new visualizers. As a result, it is more convenient for the users to explore the models and datasets now.
+
+5. **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmocr.readthedocs.io/en/dev-1.x/).
+
+Find more new features in [1.x branch](https://github.com/open-mmlab/mmocr/tree/1.x). Issues and PRs are welcome!
+
 ## Installation
 
 MMOCR depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).

README_zh-CN.md

@@ -72,6 +72,8 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处
 
 ## 最新进展
 
+### 💎 稳定版本
+
 最新的月度版本 v0.6.1 在 2022.08.04 发布。
 
 1. 对文本检测和识别任务，新支持了 ArT 数据集。
@@ -80,6 +82,22 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处
 
 阅读[更新日志](https://mmocr.readthedocs.io/en/latest/changelog.html)以获取更多信息。
 
+### 🌟 1.x 预览版本
+
+全新的 **v1.0.0rc0** 版本已经在 2022.09.01 发布：
+
+1. 架构升级：MMOCR 1.x 是基于 [MMEngine](https://github.com/open-mmlab/mmengine)，提供了一个通用的、强大的执行器，允许更灵活的定制，提供了统一的训练和测试入口。
+
+2. 统一接口：MMOCR 1.x 统一了数据集、模型、评估和可视化的接口和内部逻辑。支持更强的扩展性。
+
+3. 跨项目调用：受益于统一的设计，你可以使用其他OpenMMLab项目中实现的模型，如 MMDet。 我们提供了一个例子，说明如何通过 `MMDetWrapper` 使用 MMDetection 的 Mask R-CNN。查看我们的文档以了解更多细节。更多的包装器将在未来发布。
+
+4. 更强的可视化：我们提供了一系列可视化工具， 用户现在可以更方便可视化数据。
+
+5. 更多的文档和教程：我们增加了更多的教程，降低用户的学习门槛。详见[教程](https://mmocr.readthedocs.io/zh_CN/dev-1.x/)。
+
+可以在 [1.x 分支](https://github.com/open-mmlab/mmocr/tree/1.x) 获取更多新特性。欢迎试用并提出反馈。
+
 ## 安装
 
 MMOCR 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，以下是安装的简要步骤。