mindspore-lab · LiTingyu1997 · May 15, 2025
diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 <!--start-->
 <div align="center" markdown>
 
-# MindOCR
+# MindSpore OCR
 
 </div>
 <!--end-->
@@ -29,15 +29,15 @@ English | [中文](README_CN.md)
 
 <!--start-->
 ## Introduction
-MindOCR is an open-source toolbox for OCR development and application based on [MindSpore](https://www.mindspore.cn/en), which integrates series of mainstream text detection and recognition algorihtms/models, provides easy-to-use training and inference tools. It can accelerate the process of developing and deploying SoTA text detection and recognition models in real-world applications, such as DBNet/DBNet++ and CRNN/SVTR, and help fulfill the need of image-text understanding.
+MindSpore OCR is an open-source toolbox for OCR development and application based on [MindSpore](https://www.mindspore.cn/en), which integrates series of mainstream text detection and recognition algorihtms/models, provides easy-to-use training and inference tools. It can accelerate the process of developing and deploying SoTA text detection and recognition models in real-world applications, such as DBNet/DBNet++ and CRNN/SVTR, and help fulfill the need of image-text understanding.
 
 
 <details open markdown>
 <summary> Major Features </summary>
 
 - **Modular design**: We decoupled the OCR task into several configurable modules. Users can setup the training and evaluation pipelines, customize the data processing pipeline and model architectures easily by modifying just few lines of code.
-- **High-performance**: MindOCR provides a series of pretrained weights trained with optimized configurations that reach competitive performance on OCR tasks.
-- **Low-cost-to-apply**: Easy-to-use inference tools are provided in MindOCR to perform text detection and recognition tasks.
+- **High-performance**: MindSpore OCR provides a series of pretrained weights trained with optimized configurations that reach competitive performance on OCR tasks.
+- **Low-cost-to-apply**: Easy-to-use inference tools are provided in MindSpore OCR to perform text detection and recognition tasks.
 </details>
 
 The following is the corresponding `mindocr` versions and supported
@@ -59,7 +59,7 @@ mindspore versions.
 
 #### Prerequisites
 
-MindOCR is built on MindSpore AI framework and is compatible with the following framework versions. installation guideline for Training, please refer to the installation links shown below.
+MindSpore OCR is built on MindSpore AI framework and is compatible with the following framework versions. installation guideline for Training, please refer to the installation links shown below.
 
 - mindspore [[install](https://www.mindspore.cn/install)] Please install correct MindSpore version refer to `mindocr` versions.
 - python >= 3.7
@@ -161,7 +161,7 @@ pip install mindocr
 
 ### 1. Text Detection and Recognition Demo
 
-After installing MindOCR, we can run text detection and recognition on an arbitrary image easily as follows.
+After installing MindSpore OCR, we can run text detection and recognition on an arbitrary image easily as follows.
 
 ```shell
 python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_imgs} \
@@ -191,7 +191,7 @@ python tools/train.py --config {path/to/model_config.yaml}
 
 The `--config` arg specifies the path to a yaml file that defines the model to be trained and the training strategy including data process pipeline, optimizer, lr scheduler, etc.
 
-MindOCR provides SoTA OCR models with their training strategies in `configs` folder.
+MindSpore OCR provides SoTA OCR models with their training strategies in `configs` folder.
 You may adapt it to your task/dataset, for example, by running
 
 ```shell
@@ -216,7 +216,7 @@ For more illustration and usage, please refer to the model training section in [
 
 ### 3. Model Offline Inference
 
-You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Third-party models** (PaddleOCR, MMOCR, etc.). Please refer to [Model Offline Inference Tutorial](docs/en/inference/inference_tutorial.md)
+You can do MindSpore Lite inference in MindSpore OCR using **MindSpore OCR models** or **Third-party models** (PaddleOCR, MMOCR, etc.). Please refer to [Model Offline Inference Tutorial](docs/en/inference/inference_tutorial.md)
 
 ## Tutorials
 
@@ -295,11 +295,11 @@ You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Thi
 
 For the detailed performance of the trained models, please refer to [configs](configs).
 
-For details of MindSpore Lite inference models support, please refer to [MindOCR Models Support List](docs/en/inference/mindocr_models_list.md) and [Third-party Models Support List](docs/en/inference/thirdparty_models_list.md) (PaddleOCR etc.).
+For details of MindSpore Lite inference models support, please refer to [MindSpore OCR Models Support List](docs/en/inference/mindocr_models_list.md) and [Third-party Models Support List](docs/en/inference/thirdparty_models_list.md) (PaddleOCR etc.).
 
 ## Dataset List
 
-MindOCR provides a [dataset conversion tool](https://github.com/mindspore-lab/mindocr/blob/main/tools/dataset_converters) to OCR datasets with different formats and support customized dataset by users. We have validated the following public OCR datasets in model training/evaluation.
+MindSpore OCR provides a [dataset conversion tool](https://github.com/mindspore-lab/mindocr/blob/main/tools/dataset_converters) to OCR datasets with different formats and support customized dataset by users. We have validated the following public OCR datasets in model training/evaluation.
 
 <details close markdown>
 <summary>General OCR Datasets</summary>
@@ -461,7 +461,7 @@ which can be enabled by add "shape_list" to the `eval.dataset.output_columns` li
 
 ### How to Contribute
 
-We appreciate all kinds of contributions including issues and PRs to make MindOCR better.
+We appreciate all kinds of contributions including issues and PRs to make MindSpore OCR better.
 
 Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline. Please follow the [Model Template and Guideline](mindocr/models/README.md) for contributing a model that fits the overall interface :)
 

diff --git a/README_CN.md b/README_CN.md
@@ -1,7 +1,7 @@
 <!--start-->
 <div align="center" markdown>
 
-# MindOCR
+# MindSpore OCR
 </div>
 
 <!--end-->
@@ -30,15 +30,15 @@
 
 <!--start-->
 ## 简介
-MindOCR是一个基于[MindSpore](https://www.mindspore.cn/) 框架开发的OCR开源工具箱，集成系列主流文字检测识别的算法、模型，并提供易用的训练和推理工具，可以帮助用户快速开发和应用业界SoTA文本检测、文本识别模型，如DBNet/DBNet++和CRNN/SVTR，满足图像文档理解的需求。
+MindSpore OCR是一个基于[MindSpore](https://www.mindspore.cn/) 框架开发的OCR开源工具箱，集成系列主流文字检测识别的算法、模型，并提供易用的训练和推理工具，可以帮助用户快速开发和应用业界SoTA文本检测、文本识别模型，如DBNet/DBNet++和CRNN/SVTR，满足图像文档理解的需求。
 
 
 <details open markdown>
 <summary> 主要特性 </summary>
 
-- **模块化设计**: MindOCR将OCR任务解耦成多个可配置模块，用户只需修改几行代码，就可以轻松地在定制化的数据和模型上配置训练、评估的全流程；
-- **高性能**: MindOCR提供的预训练权重和训练方法可以使其达到OCR任务上具有竞争力的表现；
-- **易用性**: MindOCR提供易用工具帮助在真实世界数据中进行文本的检测和识别。
+- **模块化设计**: MindSpore OCR将OCR任务解耦成多个可配置模块，用户只需修改几行代码，就可以轻松地在定制化的数据和模型上配置训练、评估的全流程；
+- **高性能**: MindSpore OCR提供的预训练权重和训练方法可以使其达到OCR任务上具有竞争力的表现；
+- **易用性**: MindSpore OCR提供易用工具帮助在真实世界数据中进行文本的检测和识别。
 </details>
 
 以下是对应的“mindocr”版本和支持 Mindspore 版本。
@@ -58,7 +58,7 @@ MindOCR是一个基于[MindSpore](https://www.mindspore.cn/) 框架开发的OCR
 
 #### MindSpore相关环境准备
 
-MindOCR基于MindSpore AI框架开发，并适配以下框架版本。模型训练场景：
+MindSpore OCR基于MindSpore AI框架开发，并适配以下框架版本。模型训练场景：
 
 - mindspore [[安装](https://www.mindspore.cn/install)] 请按照mindocr分支安装对应版本MindSpore。
 - python >= 3.7
@@ -163,7 +163,7 @@ pip install mindocr
 
 ### 1. 文字检测和识别示例
 
-安装完MindOCR后，我们就很方便地进行任意图像的文本检测和识别，如下。
+安装完MindSpore OCR后，我们就很方便地进行任意图像的文本检测和识别，如下。
 
 ```shell
 python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_imgs} \
@@ -190,7 +190,7 @@ python tools/train.py --config {path/to/model_config.yaml}
 ```
 `--config` 参数用于指定yaml文件的路径，该文件定义要训练的模型和训练策略，包括数据处理流程、优化器、学习率调度器等。
 
-MindOCR在`configs`文件夹中提供系列SoTA的OCR模型及其训练策略，用户可以快速将其适配到自己的任务或数据集上，参考例子如下
+MindSpore OCR在`configs`文件夹中提供系列SoTA的OCR模型及其训练策略，用户可以快速将其适配到自己的任务或数据集上，参考例子如下
 
 ```shell
 # train text detection model DBNet++ on icdar15 dataset
@@ -220,7 +220,7 @@ python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_img
 
 ### 3. 模型离线推理
 
-你可以在MindOCR中对 **MindOCR原生模型** 或 **第三方模型**（如PaddleOCR、MMOCR等）进行MindSpore Lite推理。详情请参考[模型离线推理教程](docs/zh/inference/inference_tutorial.md)。
+你可以在MindSpore OCR中对 **MindSpore OCR原生模型** 或 **第三方模型**（如PaddleOCR、MMOCR等）进行MindSpore Lite推理。详情请参考[模型离线推理教程](docs/zh/inference/inference_tutorial.md)。
 
 ## <span id="使用教程">使用教程</span>
 
@@ -298,11 +298,11 @@ python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_img
 关于以上模型的具体训练方法和结果，请参见[configs](configs)下各模型子目录的readme文档。
 
 [MindSpore Lite](https://www.mindspore.cn/lite)模型推理的支持列表，
-请参见[MindOCR原生模型推理支持列表](docs/zh/inference/mindocr_models_list.md) 和 [第三方模型推理支持列表](docs/zh/inference/thirdparty_models_list.md)（如PaddleOCR）。
+请参见[MindSpore OCR原生模型推理支持列表](docs/zh/inference/mindocr_models_list.md) 和 [第三方模型推理支持列表](docs/zh/inference/thirdparty_models_list.md)（如PaddleOCR）。
 
 ## 数据集列表
 
-MindOCR提供了[数据格式转换工具](https://github.com/mindspore-lab/mindocr/blob/main/tools/dataset_converters) ，以支持不同格式的OCR数据集，支持用户自定义的数据集。
+MindSpore OCR提供了[数据格式转换工具](https://github.com/mindspore-lab/mindocr/blob/main/tools/dataset_converters) ，以支持不同格式的OCR数据集，支持用户自定义的数据集。
 当前已在模型训练评估中验证过的公开OCR数据集如下。
 
 <details close markdown>
@@ -463,7 +463,7 @@ MindOCR提供了[数据格式转换工具](https://github.com/mindspore-lab/mind
 
 ### 如何贡献
 
-我们欢迎包括问题单和PR在内的所有贡献，来让MindOCR变得更好。
+我们欢迎包括问题单和PR在内的所有贡献，来让MindSpore OCR变得更好。
 
 请参考[CONTRIBUTING.md](CONTRIBUTING_CN.md)作为贡献指南，请按照[Model Template and Guideline](mindocr/models/README_CN.md)的指引贡献一个适配所有接口的模型，多谢合作。