docs: Update README

README.md

@@ -32,20 +32,20 @@
 
 ### Introduction
 
-💖 The fastest running, most widely supported, completely open source and free multi-platform, multi-language OCR known to support rapid offline deployment. It features ONNXRuntime inference engine inference, which is 4~5 times faster than PaddlePaddle inference engine and has no memory leak problem.
+💖 Introducing the foremost multi-platform, multi-lingual OCR tool that boasts unparalleled speed, expansive support, and complete openness. This exceptional software is entirely free and renowned for facilitating swift offline deployments. Core to its efficiency is the ONNXRuntime inference engine, offering 4 to 5 times the speed of PaddlePaddle's engine while ensuring no memory leaks.
 
-**Supported Languages**: The default is Chinese and English, other language recognition requires self-service conversion. For specific reference [here](https://rapidai.github.io/RapidOCRDocs/blog/2022/09/28/%E6%94%AF%E6%8C%81%E8%AF%86%E5%88%AB%E8%AF%AD%E8%A8%80/).
+🦜 **Supported Languages**: It inherently supports Chinese and English, with self-service conversion required for additional languages. Please refer [here](https://rapidai.github.io/RapidOCRDocs/blog/2022/09/28/%E6%94%AF%E6%8C%81%E8%AF%86%E5%88%AB%E8%AF%AD%E8%A8%80/) for specific language support details.
 
-**Cause**: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) is not well engineered, and to make it easier for people to do OCR inference on various ends, we converted the model in PaddleOCR to ONNX format and ported it to various platforms using `Python/C++/Java/C#`.
+🔎 **Rationale**: Acknowledging the limitations in [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)'s architecture, we embarked on a mission to simplify OCR inference across diverse platforms. This endeavor culminated in converting PaddleOCR's model to the versatile ONNX format and seamlessly integrating it into Python, C++, Java, and C# environments.
 
-**Name Source**: Light, fast, economical and smart. OCR technology based on deep learning technology focuses on artificial intelligence advantages and small models, with speed as the mission and effect as the leading role.
+🎓 **Etymology**: Derived from its essence, RapidOCR embodies lightness, velocity, affordability, and intelligence. Rooted in deep learning, this OCR technology underscores AI's prowess and emphasizes compact models, prioritizing swiftness without compromising efficacy.
 
-**Usage**:
+😉 **Usage Scenarios**:
 
-- If the existing model in the repo meets the requirements → RapidOCR deployment can be used.
-- Not meeting requirements → Based on PaddleOCR. Fine-tune your own data → RapidOCR deployment.
+- **Instant Deployment**: If the pre-existing models within our repository suffice, simply leverage RapidOCR for swift deployment.
+- **Customization**: In case of specific requirements, refine PaddleOCR with your data and proceed with RapidOCR deployment, ensuring tailored results.
 
-If this repo is helpful to you, please click on a small star ⭐ Bah!
+If our repository proves beneficial to your endeavors, kindly consider leaving a star ⭐ on GitHub to show your appreciation. It means the world to us!
 
 ### Visualization ([more](https://rapidai.github.io/RapidOCRDocs/visualization/))
 
@@ -85,7 +85,7 @@ Full documentation can be found on [docs](https://rapidai.github.io/RapidOCRDocs
 - Many thanks to [DeadWood8](https://github.com/DeadWood8) for providing the [document](https://rapidai.github.io/RapidOCRDocs/install_usage/rapidocr_web/nuitka_package) which packages rapidocr_web to exe by Nuitka.
 - Many thanks to [Loovelj](https://github.com/Loovelj) for fixing the bug of sorting the text boxes. For details see [issue 75](https://github.com/RapidAI/RapidOCR/issues/75).
 
-### Code Contributors
+### 🎖 Code Contributors
 
 <p align="left">
   <a href="https://github.com/RapidAI/RapidOCR/graphs/contributors">
@@ -104,7 +104,7 @@ Full documentation can be found on [docs](https://rapidai.github.io/RapidOCRDocs
 > </div>
 
 |                                                                    Sponsor                                                                     |                                                                       Applied Products                                                                        |
-| :--------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| :-------: | :----------: |
 | <a href="https://github.com/cuiliang" title="cuiliang"><img src="https://avatars.githubusercontent.com/u/1972649?v=4" width=65 height=65></a>  | <a href="https://getquicker.net/" title="Quicker"><img src="https://github.com/RapidAI/RapidOCR/releases/download/v1.1.0/Quicker.jpg" width=65 height=65></a> |
 | <a href="https://github.com/Eunsolfs" title="Eunsolfs"><img src="https://avatars.githubusercontent.com/u/53815751?v=4" width=65 height=65></a> |                                                                               -                                                                               |
 
@@ -121,7 +121,7 @@ If you find this project useful in your research, please consider cite:
 }
 ```
 
-### Stargazers over time
+### ⭐️ Stargazers over time
 
 [![Stargazers over time](https://starchart.cc/RapidAI/RapidOCR.svg)](https://starchart.cc/RapidAI/RapidOCR)
 

docs/README_zh.md

@@ -33,20 +33,20 @@
 
 ### 简介
 
-💖目前已知**运行速度最快、支持最广**，完全开源免费并支持离线快速部署的多平台多语言OCR。主打ONNXRuntime推理引擎推理，比PaddlePaddle推理引擎速度有4~5倍提升，且没有内存泄露问题。
+💖目前，我们自豪地推出了运行速度最为迅猛、兼容性最为广泛的多平台多语言OCR工具，它完全开源免费，并支持离线环境下的快速部署。其核心亮点在于采用ONNXRuntime作为推理引擎，相比传统的PaddlePaddle推理引擎，速度实现了4至5倍的提升，同时彻底解决了内存泄露问题，确保了高效稳定的运行。
 
-**支持语言**：默认是中英文，其他语言识别需要自助转换。具体参考[这里](https://rapidai.github.io/RapidOCRDocs/blog/2022/09/28/%E6%94%AF%E6%8C%81%E8%AF%86%E5%88%AB%E8%AF%AD%E8%A8%80/)
+🦜 **支持语言概览：** 默认支持中文与英文识别，对于其他语言的识别需求，我们提供了便捷的自助转换方案。具体转换指南，请参见[这里](https://rapidai.github.io/RapidOCRDocs/blog/2022/09/28/%E6%94%AF%E6%8C%81%E8%AF%86%E5%88%AB%E8%AF%AD%E8%A8%80/)。
 
-**缘起**：因为[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)工程化尚有提升空间，为了方便在各种端上进行OCR推理，我们将PaddleOCR中的模型转换为ONNX格式，使用`Python/C++/Java/C#` 将它移植到各个平台，方便大家使用。
+🔎 **项目缘起：** 鉴于[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)R在工程化方面仍有进一步优化的空间，为了简化并加速在各种终端设备上进行OCR推理的过程，我们创新地将PaddleOCR中的模型转换为了高度兼容的ONNX格式，并利用Python、C++、Java、C#等多种编程语言，实现了跨平台的无缝移植，让广大开发者能够轻松上手，高效应用。
 
-**名称来源**：轻快好省并智能。基于深度学习的OCR技术，主打人工智能优势及小模型，以速度为使命，效果为导向。
+🎓 **名称寓意：** RapidOCR，这一名称蕴含着我们对产品的深刻期待——轻快（操作简便，响应迅速）、好省（资源占用低，成本效益高）并智能（基于深度学习的强大技术，精准高效）。我们专注于发挥人工智能的优势，打造小巧而强大的模型，将速度视为不懈追求，同时确保识别效果的卓越。
 
-**使用**：
+😉 **使用指南：**
 
-- 如果仓库下已有模型满足要求 → RapidOCR部署使用即可。
-- 不满足要求 → 基于PaddleOCR在自己数据上微调 → RapidOCR部署。
+- 直接部署：若本仓库中已提供的模型能满足您的需求，那么您只需参考[官方文档](https://rapidai.github.io/RapidOCRDocs/quickstart/)进行RapidOCR的部署与使用即可。
+- 定制化微调：若现有模型无法满足您的特定需求，您可以在PaddleOCR的基础上，利用自己的数据进行微调，随后再将其应用于RapidOCR的部署中，实现个性化定制。
 
-如果该仓库有帮助到你，还请点个小星星⭐呗！
+如果您发现本仓库对您的项目或学习有所助益，恳请您慷慨地给个小星星⭐，给予我们支持与鼓励！
 
 ### 效果展示 ([more](https://rapidai.github.io/RapidOCRDocs/visualization/))