docs: update README

README-CN.md

@@ -32,20 +32,20 @@
 
 ### 📝 简介
 
-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。
+RapidOCR 是一款完全开源免费、支持离线快速部署的多平台多语言 OCR 工具，以极致的速度与广泛的兼容性为核心优势。
 
-**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/main/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：** 默认支持中英文识别。其他支持的语言，参见文档：[模型列表](https://rapidai.github.io/RapidOCRDocs/main/model_list/)
 
-**项目缘起：** 鉴于[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)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。
+**项目缘起：** 鉴于 [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) 在工程化方面仍有优化空间，为简化并加速 OCR 模型在各类终端设备上的推理部署，我们创新性地将 PaddleOCR 中的模型转换为高度兼容的 ONNX 格式，并基于 Python、C++、Java、C# 等多种编程语言，实现了跨平台的无缝移植，让开发者能够轻松上手、高效集成。
 
-**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。
+**名称寓意：** RapidOCR 这一名称承载着我们对产品的核心期待——轻快（操作简便、响应迅速）、好省（资源占用低、成本效益高）且智能（依托深度学习技术，实现精准高效的识别）。我们专注于发挥人工智能的优势，打造小巧而强大的模型，始终将速度作为不懈追求，同时确保卓越的识别效果。
 
 **使用指南：**
 
-- 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/main/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。
+- **直接部署**：若本仓库提供的模型已满足您的需求，只需参考 [快速开始](https://rapidai.github.io/RapidOCRDocs/main/quickstart/) 即可快速完成 RapidOCR 的部署与使用。
+- **定制化微调**：若现有模型无法满足特定场景需求，您可在 PaddleOCR 基础上，利用自有数据进行微调，再将优化后的模型应用于 RapidOCR 的部署流程，实现个性化定制。
 
-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！
+如果您觉得本项目对您的工作或学习有所帮助，恳请您不吝赐予一颗 ⭐ Star，给予我们宝贵的支持与鼓励！
 
 ### 🎥 效果展示
 

README.md

@@ -33,20 +33,20 @@ Join our [Discord](https://discord.gg/33eyQJq498)
 
 ### 📝 Introduction
 
-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.
+RapidOCR is a completely open-source, free OCR tool that supports multi-platform, multi-language operation and rapid offline deployment. Its core advantages lie in extreme speed and extensive compatibility.
 
-**Supported Languages**: It inherently supports Chinese and English, with self-service conversion required for additional languages. Please refer [here](https://rapidai.github.io/RapidOCRDocs/main/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.
+**Supported Languages:** Default support for Chinese and English recognition. For other supported languages, please refer to the documentation: [Model List](https://rapidai.github.io/RapidOCRDocs/main/model_list/).
 
-**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.
+**Project Origin:** Considering that [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) still has room for optimization in engineering aspects, we innovatively converted the models in PaddleOCR into the highly compatible ONNX format to simplify and accelerate the inference deployment of OCR models on various terminal devices. Furthermore, we achieved seamless cross-platform porting based on multiple programming languages such as Python, C++, Java, and C#, enabling developers to get started easily and integrate efficiently.
 
-**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.
+**Name Implication:** The name "RapidOCR" embodies our core expectations for the product: **Rapid** (simple operation, fast response), **Good & Economical** (low resource consumption, high cost-effectiveness), and **Intelligent** (achieving accurate and efficient recognition relying on deep learning technology). We focus on leveraging the advantages of artificial intelligence to create compact yet powerful models, relentlessly pursuing speed while ensuring excellent recognition results.
 
-**Usage Scenarios**:
+**User Guide:**
 
-- **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.
+- **Direct Deployment:** If the models provided in this repository meet your needs, simply refer to the [Official Documentation](https://rapidai.github.io/RapidOCRDocs/main/quickstart/) to quickly complete the deployment and usage of RapidOCR.
+- **Custom Fine-tuning:** If the existing models cannot meet specific scenario requirements, you can fine-tune them using your own data based on PaddleOCR, and then apply the optimized models to the RapidOCR deployment process to achieve personalized customization.
 
-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!
+If you find this project helpful for your work or study, we kindly ask you to give us a ⭐ Star to provide valuable support and encouragement!
 
 ### 🎥 Visualization