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<h1>Home</h1>
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<h2 id="_1">简介<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h2>
<p>PaddleOCR 旨在打造一套丰富、领先、且实用的 OCR 工具库，助力开发者训练出更好的模型，并应用落地。</p>
<h2 id="_2">🚀 社区<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<p>PaddleOCR 由 <a href="https://github.com/PaddlePaddle/PaddleOCR/issues/12122">PMC</a> 监督。Issues 和 PRs 将在尽力的基础上进行审查。欲了解 PaddlePaddle 社区的完整概况，请访问 <a href="https://github.com/PaddlePaddle/community">community</a>。</p>
<p>⚠️注意：<a href="https://github.com/PaddlePaddle/PaddleOCR/issues">Issues</a>模块仅用来报告程序🐞Bug，其余提问请移步<a href="https://github.com/PaddlePaddle/PaddleOCR/discussions">Discussions</a>模块提问。如所提Issue不是Bug，会被移到Discussions模块，敬请谅解。</p>
<h2 id="_3">📣 近期更新<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>🔥2024.10.1 添加OCR领域低代码全流程开发能力</strong>:</li>
<li>
<p>飞桨低代码开发工具PaddleX，依托于PaddleOCR的先进技术，支持了OCR领域的低代码全流程开发能力：</p>
<ul>
<li>🎨 <a href="https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/quick_start.html"><strong>模型丰富一键调用</strong></a>：将文本图像智能分析、通用OCR、通用版面解析、通用表格识别、公式识别、印章文本识别涉及的<strong>17个模型</strong>整合为6条模型产线，通过极简的<strong>Python API一键调用</strong>，快速体验模型效果。此外，同一套API，也支持图像分类、目标检测、图像分割、时序预测等共计<strong>200+模型</strong>，形成20+单功能模块，方便开发者进行<strong>模型组合</strong>使用。</li>
<li>🚀<a href="https://paddlepaddle.github.io/PaddleOCR/latest/paddlex/overview.html"><strong>提高效率降低门槛</strong></a>：提供基于<strong>统一命令</strong>和<strong>图形界面</strong>两种方式，实现模型简洁高效的使用、组合与定制。支持<strong>高性能推理、服务化部署和端侧部署</strong>等多种部署方式。此外，对于各种主流硬件如<strong>英伟达GPU、昆仑芯、昇腾、寒武纪和海光</strong>等，进行模型开发时，都可以<strong>无缝切换</strong>。</li>
</ul>
</li>
<li>
<p>支持文档场景信息抽取v3<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.md">PP-ChatOCRv3-doc</a>、基于RT-DETR的<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md">高精度版面区域检测模型</a>和PicoDet的<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/layout_detection.md">高效率版面区域检测模型</a>、高精度表格结构识别模型<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md">SLANet_Plus</a>、文本图像矫正模型<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md">UVDoc</a>、公式识别模型<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/formula_recognition.md">LatexOCR</a>、基于PP-LCNet的<a href="https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md">文档图像方向分类模型</a></p>
</li>
<li>
<p><strong>🔥2024.7 添加 PaddleOCR 算法模型挑战赛冠军方案</strong>：</p>
<ul>
<li>赛题一：OCR 端到端识别任务冠军方案——<a href="https://paddlepaddle.github.io/PaddleOCR/latest/algorithm/text_recognition/algorithm_rec_svtrv2.html">场景文本识别算法-SVTRv2</a>；</li>
<li>赛题二：通用表格识别任务冠军方案——<a href="https://paddlepaddle.github.io/PaddleOCR/latest/algorithm/table_recognition/algorithm_table_slanet.html">表格识别算法-SLANet-LCNetV2</a>。</li>
</ul>
</li>
</ul>
<blockquote>
<p><a href="update.html">更多</a></p>
</blockquote>
<h2 id="_4">🌟 特性<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h2>
<p>支持多种 OCR 相关前沿算法，在此基础上打造产业级特色模型PP-OCR、PP-Structure和PP-ChatOCR，并打通数据生产、模型训练、压缩、预测部署全流程。</p>
<p><img src="./images/ppocrv4.png" width="600" /></p>
<h2 id="_5">效果展示<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h2>
<h3 id="pp-ocrv3">超轻量PP-OCRv3效果展示<a class="headerlink" href="#pp-ocrv3" title="Permanent link">&para;</a></h3>
<h4 id="pp-ocrv3_1">PP-OCRv3中文模型<a class="headerlink" href="#pp-ocrv3_1" title="Permanent link">&para;</a></h4>
<p><img alt="img" src="images/test_add_91.jpg" /></p>
<p><img src="./images/00006737.jpg" width="600" /></p>
<p><img src="./images/PP-OCRv3-pic001.jpg" width="600" /></p>
<p><img src="./images/PP-OCRv3-pic002.jpg" width="600" /></p>
<p><img src="./images/PP-OCRv3-pic003.jpg" width="600" /></p>
<h4 id="pp-ocrv3_2">PP-OCRv3英文数字模型<a class="headerlink" href="#pp-ocrv3_2" title="Permanent link">&para;</a></h4>
<p><img src="./images/en_1.png" width="600" /></p>
<p><img src="./images/en_2.png" width="600" /></p>
<p><img src="./images/en_3-0398013.png" width="600" /></p>
<h4 id="pp-ocrv3_3">PP-OCRv3多语言模型<a class="headerlink" href="#pp-ocrv3_3" title="Permanent link">&para;</a></h4>
<p><img src="./images/japan_2.jpg" width="600" /></p>
<p><img src="./images/korean_1.jpg" width="600" /></p>
<h4 id="pp-structure">PP-Structure 文档分析<a class="headerlink" href="#pp-structure" title="Permanent link">&para;</a></h4>
<ul>
<li>版面分析+表格识别</li>
</ul>
<p><img src="./images/ppstructure-20240708082235651.gif" width="600" /></p>
<ul>
<li>SER（语义实体识别）</li>
</ul>
<p><img src="./images/185310636-6ce02f7c-790d-479f-b163-ea97a5a04808-20240708082238739.jpg" width="600" /></p>
<p><img src="./images/185539517-ccf2372a-f026-4a7c-ad28-c741c770f60a-20240708082247529.png" width="600" /></p>
<p><img src="./images/197464552-69de557f-edff-4c7f-acbf-069df1ba097f-20240708082253634.png" width="600" /></p>
<ul>
<li>RE（关系提取）</li>
</ul>
<p><img src="./images/185393805-c67ff571-cf7e-4217-a4b0-8b396c4f22bb-20240708082310650.jpg" width="600" /></p>
<p><img src="./images/185540080-0431e006-9235-4b6d-b63d-0b3c6e1de48f-20240708082316558.jpg" width="600" /></p>
<p><img src="./images/186094813-3a8e16cc-42e5-4982-b9f4-0134dfb5688d-20240708082323916.png" width="600" /></p>
<h2 id="_6">许可证书<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<p>本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。</p>
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