fix typos (#17695)

Co-authored-by: Lin Manhui <mhlin425@whu.edu.cn>

Author: Echo-Nie

README.md

@@ -101,7 +101,7 @@ In addition to providing an outstanding model library, PaddleOCR 3.0 also offers
 
 - Released PaddleOCR-VL:
     - **Model Introduction**:
-        - **PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. **This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption**. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. The model has been released on [HuggingFace](https://huggingface.co/PaddlePaddle/PaddleOCR-VL). Everyone is welcome to download and use it! More introduction infomation can be found in [PaddleOCR-VL](https://www.paddleocr.ai/latest/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.html).
+        - **PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. **This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption**. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. The model has been released on [HuggingFace](https://huggingface.co/PaddlePaddle/PaddleOCR-VL). Everyone is welcome to download and use it! More introduction information can be found in [PaddleOCR-VL](https://www.paddleocr.ai/latest/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.html).
 
     - **Core Features**:
         - **Compact yet Powerful VLM Architecture**: We present a novel vision-language model that is specifically designed for resource-efficient inference, achieving outstanding performance in element recognition. By integrating a NaViT-style dynamic high-resolution visual encoder with the lightweight ERNIE-4.5-0.3B language model, we significantly enhance the model’s recognition capabilities and decoding efficiency. This integration maintains high accuracy while reducing computational demands, making it well-suited for efficient and practical document processing applications.

docs/community/code_and_doc.en.md

@@ -244,7 +244,7 @@ git add README.md
 pre-commit
 ```
 
-Repeat these steps until the pre-comit format check does not error. As shown below.
+Repeat these steps until the pre-commit format check does not error. As shown below.
 
 ![img](./images/precommit_pass.png)
 

docs/community/code_and_doc.md

@@ -242,7 +242,7 @@ git add README.md
 pre-commit
 ```
 
-重复上述步骤，直到pre-comit格式检查不报错。如下所示。
+重复上述步骤，直到pre-commit格式检查不报错。如下所示。
 
 ![img](./images/precommit_pass.png)
 

docs/version2.x/legacy/paddle_cloud.md

@@ -211,7 +211,7 @@ $ helm repo update
 # 安装云上飞桨组件
 $ helm install pdc paddlecloud/paddlecloud --set tags.all-dep=true --namespace paddlecloud --create-namespace
 
-# 检查所有云上飞桨组件是否成功启动，命名空间下的所有Pod都为Runing状态则安装成功。
+# 检查所有云上飞桨组件是否成功启动，命名空间下的所有Pod都为Running状态则安装成功。
 $ kubectl get pods -n paddlecloud
 NAME                                                 READY   STATUS    RESTARTS   AGE
 pdc-hostpath-5b6bd6787d-bxvxg                        1/1     Running   0          10h

docs/version2.x/ppocr/installation.md

@@ -73,4 +73,4 @@ pip3 install -r requirements.txt
 ```
 
 注意，windows环境下，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装，
-直接通过pip安装的shapely库可能出现`[winRrror 126] 找不到指定模块的问题`。
+直接通过pip安装的shapely库可能出现`[winError 126] 找不到指定模块的问题`。

docs/version2.x/ppocr/quick_start.md

@@ -34,7 +34,7 @@ comments: true
 pip install "paddleocr<3.0"
 ```
 
-- 对于Windows环境用户：直接通过pip安装的shapely库可能出现`[winRrror 126] 找不到指定模块的问题`。建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装。
+- 对于Windows环境用户：直接通过pip安装的shapely库可能出现`[winError 126] 找不到指定模块的问题`。建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装。
 
 ## 2. 便捷使用
 

docs/version3.x/pipeline_usage/PaddleOCR-VL-KUNLUNXIN-XPU.md

@@ -65,7 +65,7 @@ python -m pip install -U "paddleocr[doc-parser]"
 
 ## 2. 快速开始
 
-请参考[PaddleOCR-VL 使用教程](./PaddleOCR-VL.md)相同章节，注意需要指定 `deivce="xpu"`。
+请参考[PaddleOCR-VL 使用教程](./PaddleOCR-VL.md)相同章节，注意需要指定 `device="xpu"`。
 
 ## 3. 使用推理加速框架提升 VLM 推理性能
 

docs/version3.x/pipeline_usage/PaddleOCR-VL-MetaX-GPU.md

@@ -68,7 +68,7 @@ python -m pip install -U "paddleocr[doc-parser]"
 
 ## 2. 快速开始
 
-请参考 [PaddleOCR-VL 使用教程](./PaddleOCR-VL.md) 相同章节，注意需要指定 `deivce="metax_gpu"`。
+请参考 [PaddleOCR-VL 使用教程](./PaddleOCR-VL.md) 相同章节，注意需要指定 `device="metax_gpu"`。
 
 ## 3. 使用推理加速框架提升 VLM 推理性能