add gcu pipeline list (#3341)

docs/support_list/pipelines_list_gcu.en.md

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+---
+comments: true
+---
+
+# PaddleX Pipelines (GCU)
+
+## 1. Basic Pipelines
+
+<table>
+  <tr>
+    <th width="10%">Pipeline Name</th>
+    <th width="10%">Pipeline Modules</th>
+    <th width="10%">Baidu AIStudio Community Experience URL</th>
+    <th width="50%">Pipeline Introduction</th>
+    <th width="20%">Applicable Scenarios</th>
+  </tr>
+  <tr>
+    <td>General Image Classification</td>
+    <td>Image Classification</td>
+    <td><a href="https://aistudio.baidu.com/community/app/100061/webUI">Online Experience</a></td>
+    <td>Image classification is a technique that assigns images to predefined categories. It is widely used in object recognition, scene understanding, and automatic annotation. Image classification can identify various objects such as animals, plants, traffic signs, etc., and categorize them based on their features. By leveraging deep learning models, image classification can automatically extract image features and perform accurate classification. The General Image Classification Pipeline is designed to solve image classification tasks for given images.</td>
+    <td>
+      <ul>
+        <li>Automatic classification and recognition of product images</li>
+        <li>Real-time monitoring of defective products on production lines</li>
+        <li>Personnel recognition in security surveillance</li>
+      </ul>
+    </td>
+  </tr>
+  <tr>
+    <td>General Object Detection</td>
+    <td>Object Detection</td>
+    <td><a href="https://aistudio.baidu.com/community/app/70230/webUI">Online Experience</a></td>
+    <td>Object detection aims to identify the categories and locations of multiple objects in images or videos by generating bounding boxes to mark these objects. Unlike simple image classification, object detection not only recognizes what objects are in the image, such as people, cars, and animals, but also accurately determines the specific location of each object, usually represented by a rectangular box. This technology is widely used in autonomous driving, surveillance systems, and smart photo albums, relying on deep learning models (e.g., YOLO, Faster R-CNN) that efficiently extract features and perform real-time detection, significantly enhancing the computer's ability to understand image content.</td>
+    <td>
+      <ul>
+        <li>Tracking moving objects in video surveillance</li>
+        <li>Vehicle detection in autonomous driving</li>
+        <li>Defect detection in industrial manufacturing</li>
+        <li>Shelf product detection in retail</li>
+      </ul>
+    </td>
+  </tr>
+  <tr>
+    <td rowspan = 2>General OCR</td>
+    <td >Text Detection</td>
+    <td rowspan = 2><a href="https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">Online Experience</a></td>
+    <td rowspan = 2>OCR (Optical Character Recognition) is a technology that converts text in images into editable text. It is widely used in document digitization, information extraction, and data processing. OCR can recognize printed text, handwritten text, and even certain types of fonts and symbols. The General OCR Pipeline is designed to solve text recognition tasks, extracting text information from images and outputting it in text form. PP-OCRv4 is an end-to-end OCR system that achieves millisecond-level text content prediction on CPUs, achieving state-of-the-art (SOTA) performance in general scenarios. Based on this project, developers from academia, industry, and research have quickly implemented various OCR applications covering general, manufacturing, finance, transportation.</td>
+    <td rowspan = 2>
+      <ul>
+        <li>Document digitization</li>
+        <li>Information extraction</li>
+        <li>Data processing</li>
+      </ul>
+    </td>
+  </tr>
+    <tr>
+    <td>Text Recognition</td>
+  </tr>
+</table>
+
+## 2. Featured Pipelines
+Not supported yet, please stay tuned!

docs/support_list/pipelines_list_gcu.md

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+---
+comments: true
+---
+
+# PaddleX产线列表(GCU)
+
+## 1、基础产线
+
+<table>
+    <tr>
+        <th width="10%">产线名称</th>
+        <th width="10%">产线模块</th>
+        <th width="10%">星河社区体验地址</th>
+        <th width="50%">产线介绍</th>
+        <th width="20%">适用场景</th>
+    </tr>
+  <tr>
+    <td>通用图像分类</td>
+    <td>图像分类</td>
+    <td><a href="https://aistudio.baidu.com/community/app/100061/webUI">在线体验</a></td>
+    <td>图像分类是一种将图像分配到预定义类别的技术。它广泛应用于物体识别、场景理解和自动标注等领域。图像分类可以识别各种物体，如动物、植物、交通标志等，并根据其特征将其归类。通过使用深度学习模型，图像分类能够自动提取图像特征并进行准确分类。</td>
+    <td>
+    <ul>
+        <li>商品图片的自动分类和识别</li>
+        <li>流水线上不合格产品的实时监控</li>
+        <li>安防监控中人员的识别</li>
+      </ul>
+  </tr>
+  <tr>
+    <td>通用目标检测</td>
+    <td>目标检测</td>
+    <td><a href="https://aistudio.baidu.com/community/app/70230/webUI">在线体验</a></td>
+    <td>目标检测旨在识别图像或视频中多个对象的类别及其位置，通过生成边界框来标记这些对象。与简单的图像分类不同，目标检测不仅需要识别出图像中有哪些物体，例如人、车和动物等，还需要准确地确定每个物体在图像中的具体位置，通常以矩形框的形式表示。该技术广泛应用于自动驾驶、监控系统和智能相册等领域，依赖于深度学习模型（如YOLO、Faster R-CNN等），这些模型能够高效地提取特征并进行实时检测，显著提升了计算机对图像内容理解的能力。</td>
+    <td>
+      <ul>
+        <li>视频监控中移动物体的跟踪</li>
+        <li>自动驾驶中车辆的检测</li>
+        <li>工业制造中缺陷产品的检测</li>
+        <li>零售业中货架商品的检测</li>
+      </ul>
+    </td>
+  </tr>
+  <tr>
+    <td rowspan = 2>通用OCR</td>
+    <td>文本检测</td>
+    <td rowspan = 2><a href="https://aistudio.baidu.com/community/app/91660/webUI?source=appMineRecent">在线体验</a></td>
+    <td rowspan = 2>OCR（光学字符识别，Optical Character Recognition）是一种将图像中的文字转换为可编辑文本的技术。它广泛应用于文档数字化、信息提取和数据处理等领域。OCR 可以识别印刷文本、手写文本，甚至某些类型的字体和符号。 通用 OCR 产线用于解决文字识别任务，提取图片中的文字信息以文本形式输出，PP-OCRv4 是一个端到端 OCR 串联系统，可实现 CPU 上毫秒级的文本内容精准预测，在通用场景上达到开源SOTA。基于该项目，产学研界多方开发者已快速落地多个 OCR 应用，使用场景覆盖通用、制造、金融、交通等各个领域。</td>
+    <td rowspan = 2>
+    <ul>
+        <li>智能安防中车牌号</li>
+        <li>门牌号等信息的识别</li>
+        <li>纸质文档的数字化</li>
+        <li>文化遗产中古代文字的识别</li>
+      </ul>
+      </td>
+  </tr>
+  <tr>
+    <td>文本识别</td>
+  </tr>
+</table>
+
+## 2、特色产线
+暂不支持，敬请期待！