PaddlePaddle · cuicheng01 · Feb 21, 2025 · Feb 20, 2025
diff --git a/docs/data_annotations/cv_modules/instance_segmentation.md b/docs/data_annotations/cv_modules/instance_segmentation.md
@@ -52,7 +52,7 @@ labelme images --labels label.txt --nodata --autosave --output annotations
 * `labels` 类别标签路径。
 * `nodata` 停止将图像数据存储到JSON文件。
 * `autosave` 自动存储。
-* `ouput` 标签文件存储路径。
+* `output` 标签文件存储路径。
 #### 2.3.3 开始图片标注
 * 启动 `labelme` 后如图所示：
 

diff --git a/docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md b/docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md
@@ -82,7 +82,7 @@ Parameter description:
 ```
 --pipeline: The name of the pipeline, here it is the 3D multi-modal fusion detection pipeline.
 
---input: The input path to the .tar file containing image and lidar data to be processed. 3D multi-modal fusion detection pipeline is a multi-input pipeline depending on images, pointclouds and transition matrix information. Tar file contains "samples" directory with all images and pointclouds data, "sweeps" directories with pointclouds data of relative frames and nuscnes_infos_val.pkl file containing relataive data path from "samples" and "sweeps" directories and transition matrix infomation.
+--input: The input path to the .tar file containing image and lidar data to be processed. 3D multi-modal fusion detection pipeline is a multi-input pipeline depending on images, pointclouds and transition matrix information. Tar file contains "samples" directory with all images and pointclouds data, "sweeps" directories with pointclouds data of relative frames and nuscnes_infos_val.pkl file containing relataive data path from "samples" and "sweeps" directories and transition matrix information.
 
 --device: The GPU index to be used (e.g., gpu:0 means using the 0th GPU, gpu:1,2 means using the 1st and 2nd GPUs), or you can choose to use CPU (--device cpu).
 ```

diff --git a/...information_extraction_pipelines/document_scene_information_extraction_v3.en.md b/...information_extraction_pipelines/document_scene_information_extraction_v3.en.md
@@ -2,7 +2,7 @@
 comments: true
 ---
 
-# PP-ChatOCRv3-doc Pipeline utorial
+# PP-ChatOCRv3-doc Pipeline Tutorial
 
 ## 1. Introduction to PP-ChatOCRv3-doc Pipeline
 PP-ChatOCRv3-doc is a unique intelligent analysis solution for documents and images developed by PaddlePaddle. It combines Large Language Models (LLM) and OCR technology to provide a one-stop solution for complex document information extraction challenges such as layout analysis, rare characters, multi-page PDFs, tables, and seal recognition. By integrating with ERNIE Bot, it fuses massive data and knowledge to achieve high accuracy and wide applicability.

diff --git a/docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.en.md b/docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.en.md
@@ -262,7 +262,7 @@ You can [experience the formula recognition pipeline online](https://aistudio.ba
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-> ❗ Before using the formula recognition pipelin locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the formula recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the effect of the formula recognition pipeline with one command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/pipelines/general_formula_recognition_001.png), and replace `--input` with the local path for prediction.