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<meta name="description" content="Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)">
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模型量化
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</a>
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模型裁剪
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</a>
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基于Python预测引擎推理
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</a>
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<a href="../ppocr/infer_deploy/cpp_infer.html" class="md-nav__link">
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<span class="md-ellipsis">
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基于C++预测引擎推理
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</span>
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</a>
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Visual Studio 2019 Community CMake 编译指南
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</span>
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</a>
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服务化部署
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</a>
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</li>
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<li class="md-nav__item">
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Android部署
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../ppocr/infer_deploy/Jetson_infer.html" class="md-nav__link">
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<span class="md-ellipsis">
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Jetson部署
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</span>
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</a>
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</li>
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<a href="../ppocr/infer_deploy/lite.html" class="md-nav__link">
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<span class="md-ellipsis">
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端侧部署
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../ppocr/infer_deploy/paddle_js.html" class="md-nav__link">
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<span class="md-ellipsis">
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网页前端部署
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</span>
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</a>
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</li>
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|
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|
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<li class="md-nav__item">
|
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<a href="../ppocr/infer_deploy/paddle2onnx.html" class="md-nav__link">
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|
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|
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<span class="md-ellipsis">
|
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Paddle2ONNX模型转化与预测
|
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</span>
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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<li class="md-nav__item">
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<a href="../ppocr/infer_deploy/paddle_cloud.html" class="md-nav__link">
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<span class="md-ellipsis">
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云上飞桨部署工具
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</span>
|
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|
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</a>
|
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</li>
|
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<li class="md-nav__item">
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<a href="../ppocr/infer_deploy/benchmark.html" class="md-nav__link">
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<span class="md-ellipsis">
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Benchmark
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|
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</a>
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<li class="md-nav__item">
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<a href="../ppocr/blog/PP-OCRv3_introduction.html" class="md-nav__link">
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<span class="md-ellipsis">
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PP-OCRv3技术报告
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</span>
|
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|
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|
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</a>
|
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</li>
|
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<li class="md-nav__item">
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<a href="../ppocr/blog/PP-OCRv4_introduction.html" class="md-nav__link">
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<span class="md-ellipsis">
|
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PP-OCRv4技术报告
|
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</span>
|
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|
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|
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</a>
|
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</li>
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<li class="md-nav__item">
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<a href="../ppocr/blog/whl.html" class="md-nav__link">
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<span class="md-ellipsis">
|
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paddleocr package使用说明
|
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</span>
|
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|
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|
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</a>
|
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</li>
|
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|
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<li class="md-nav__item">
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<a href="../ppocr/blog/multi_languages.html" class="md-nav__link">
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|
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<span class="md-ellipsis">
|
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多语言模型
|
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</span>
|
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|
||
|
||
</a>
|
||
</li>
|
||
|
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<li class="md-nav__item">
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<a href="../ppocr/blog/ocr_book.html" class="md-nav__link">
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|
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|
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<span class="md-ellipsis">
|
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动手学OCR
|
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</span>
|
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|
||
|
||
</a>
|
||
</li>
|
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|
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<li class="md-nav__item">
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<a href="../ppocr/blog/enhanced_ctc_loss.html" class="md-nav__link">
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<span class="md-ellipsis">
|
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Enhanced CTC Loss
|
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</span>
|
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|
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|
||
</a>
|
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</li>
|
||
|
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|
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|
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|
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<li class="md-nav__item">
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<a href="../ppocr/blog/slice.html" class="md-nav__link">
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<span class="md-ellipsis">
|
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切片操作
|
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</span>
|
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|
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|
||
</a>
|
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</li>
|
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|
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|
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<li class="md-nav__item">
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<a href="../ppocr/blog/inference_args.html" class="md-nav__link">
|
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|
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|
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<span class="md-ellipsis">
|
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PaddleOCR模型推理参数解释
|
||
</span>
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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<li class="md-nav__item">
|
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<a href="../ppocr/blog/distributed_training.html" class="md-nav__link">
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<span class="md-ellipsis">
|
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分布式训练
|
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</span>
|
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|
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|
||
</a>
|
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</li>
|
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|
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|
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<li class="md-nav__item">
|
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<a href="../ppocr/blog/clone.html" class="md-nav__link">
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|
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|
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<span class="md-ellipsis">
|
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项目克隆
|
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</span>
|
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|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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<li class="md-nav__item">
|
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<a href="../ppocr/blog/config.html" class="md-nav__link">
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|
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<span class="md-ellipsis">
|
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配置文件内容与生成
|
||
</span>
|
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|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
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|
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|
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<li class="md-nav__item">
|
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<a href="../ppocr/blog/customize.html" class="md-nav__link">
|
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|
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|
||
<span class="md-ellipsis">
|
||
如何生产自定义超轻量模型？
|
||
</span>
|
||
|
||
|
||
</a>
|
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</li>
|
||
|
||
|
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</ul>
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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<span class="md-ellipsis">
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PP-Structure文档分析
|
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</span>
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<span class="md-ellipsis">
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概述
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<span class="md-ellipsis">
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快速开始
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PCB文字识别
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通用中英文OCR数据集
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手写中文OCR数据集
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垂类多语言OCR数据集
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关键信息提取数据集
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1.1 文本检测算法
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<article class="md-content__inner md-typeset">
|
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|
||
|
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|
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|
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<a href="https://github.com/PaddlePaddle/PaddleOCR/edit/main/docs/algorithm/overview.md" title="编辑此页" class="md-content__button md-icon">
|
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75z"/></svg>
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<a href="https://github.com/PaddlePaddle/PaddleOCR/raw/main/docs/algorithm/overview.md" title="查看本页的源代码" class="md-content__button md-icon">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0 8a5 5 0 0 1-5-5 5 5 0 0 1 5-5 5 5 0 0 1 5 5 5 5 0 0 1-5 5m0-12.5C7 4.5 2.73 7.61 1 12c1.73 4.39 6 7.5 11 7.5s9.27-3.11 11-7.5c-1.73-4.39-6-7.5-11-7.5"/></svg>
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</a>
|
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|
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|
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|
||
<h1 id="_1">前沿算法与模型<a class="headerlink" href="#_1" title="Permanent link">¶</a></h1>
|
||
<p>本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在<strong>英文公开数据集</strong>上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考<a href="../ppocr/model_list.html">PP-OCRv3 系列模型下载</a>。</p>
|
||
<p>PaddleOCR将<strong>持续新增</strong>支持OCR领域前沿算法与模型，<strong>欢迎广大开发者合作共建，贡献更多算法。</strong></p>
|
||
<p>新增算法可参考教程：<a href="add_new_algorithm.html">使用PaddleOCR架构添加新算法</a></p>
|
||
<h2 id="1">1. 两阶段算法<a class="headerlink" href="#1" title="Permanent link">¶</a></h2>
|
||
<h3 id="11">1.1 文本检测算法<a class="headerlink" href="#11" title="Permanent link">¶</a></h3>
|
||
<p>已支持的文本检测算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_db.html">DB与DB++</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_east.html">EAST</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_sast.html">SAST</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_psenet.html">PSENet</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_fcenet.html">FCENet</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_drrg.html">DRRG</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_detection/algorithm_det_ct.html">CT</a></li>
|
||
</ul>
|
||
<p>在ICDAR2015文本检测公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>precision</th>
|
||
<th>recall</th>
|
||
<th>Hmean</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>EAST</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>88.71%</td>
|
||
<td>81.36%</td>
|
||
<td>84.88%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>EAST</td>
|
||
<td>MobileNetV3</td>
|
||
<td>78.20%</td>
|
||
<td>79.10%</td>
|
||
<td>78.65%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>DB</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>86.41%</td>
|
||
<td>78.72%</td>
|
||
<td>82.38%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>DB</td>
|
||
<td>MobileNetV3</td>
|
||
<td>77.29%</td>
|
||
<td>73.08%</td>
|
||
<td>75.12%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SAST</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>91.39%</td>
|
||
<td>83.77%</td>
|
||
<td>87.42%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>PSE</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>85.81%</td>
|
||
<td>79.53%</td>
|
||
<td>82.55%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>PSE</td>
|
||
<td>MobileNetV3</td>
|
||
<td>82.20%</td>
|
||
<td>70.48%</td>
|
||
<td>75.89%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>DB++</td>
|
||
<td>ResNet50</td>
|
||
<td>90.89%</td>
|
||
<td>82.66%</td>
|
||
<td>86.58%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams">合成数据预训练模型</a>/<a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_db%2B%2B_icdar15_train.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>在Total-text文本检测公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>precision</th>
|
||
<th>recall</th>
|
||
<th>Hmean</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>SAST</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>89.63%</td>
|
||
<td>78.44%</td>
|
||
<td>83.66%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>CT</td>
|
||
<td>ResNet18_vd</td>
|
||
<td>88.68%</td>
|
||
<td>81.70%</td>
|
||
<td>85.05%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_train.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>在CTW1500文本检测公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>precision</th>
|
||
<th>recall</th>
|
||
<th>Hmean</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>FCE</td>
|
||
<td>ResNet50_dcn</td>
|
||
<td>88.39%</td>
|
||
<td>82.18%</td>
|
||
<td>85.27%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>DRRG</td>
|
||
<td>ResNet50_vd</td>
|
||
<td>89.92%</td>
|
||
<td>80.91%</td>
|
||
<td>85.18%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p><strong>说明：</strong> SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：</p>
|
||
<ul>
|
||
<li><a href="https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw">百度云地址</a> (提取码: 2bpi)</li>
|
||
<li><a href="https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing">Google Drive下载地址</a></li>
|
||
</ul>
|
||
<h3 id="12">1.2 文本识别算法<a class="headerlink" href="#12" title="Permanent link">¶</a></h3>
|
||
<p>已支持的文本识别算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_crnn.html">CRNN</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_rosetta.html">Rosetta</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_starnet.html">STAR-Net</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_rare.html">RARE</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_srn.html">SRN</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_nrtr.html">NRTR</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_sar.html">SAR</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_seed.html">SEED</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_svtr.html">SVTR</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_vitstr.html">ViTSTR</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_abinet.html">ABINet</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_visionlan.html">VisionLAN</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_spin.html">SPIN</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_robustscanner.html">RobustScanner</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_rfl.html">RFL</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_parseq.html">ParseQ</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_cppd.html">CPPD</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="text_recognition/algorithm_rec_satrn.html">SATRN</a></li>
|
||
</ul>
|
||
<p>参考<a href="https://arxiv.org/abs/1904.01906">DTRB</a> (3)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>Avg Accuracy</th>
|
||
<th>模型存储命名</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Rosetta</td>
|
||
<td>Resnet34_vd</td>
|
||
<td>79.11%</td>
|
||
<td>rec_r34_vd_none_none_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>Rosetta</td>
|
||
<td>MobileNetV3</td>
|
||
<td>75.80%</td>
|
||
<td>rec_mv3_none_none_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>CRNN</td>
|
||
<td>Resnet34_vd</td>
|
||
<td>81.04%</td>
|
||
<td>rec_r34_vd_none_bilstm_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>CRNN</td>
|
||
<td>MobileNetV3</td>
|
||
<td>77.95%</td>
|
||
<td>rec_mv3_none_bilstm_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>StarNet</td>
|
||
<td>Resnet34_vd</td>
|
||
<td>82.85%</td>
|
||
<td>rec_r34_vd_tps_bilstm_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>StarNet</td>
|
||
<td>MobileNetV3</td>
|
||
<td>79.28%</td>
|
||
<td>rec_mv3_tps_bilstm_ctc</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>RARE</td>
|
||
<td>Resnet34_vd</td>
|
||
<td>83.98%</td>
|
||
<td>rec_r34_vd_tps_bilstm_att</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>RARE</td>
|
||
<td>MobileNetV3</td>
|
||
<td>81.76%</td>
|
||
<td>rec_mv3_tps_bilstm_att</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SRN</td>
|
||
<td>Resnet50_vd_fpn</td>
|
||
<td>86.31%</td>
|
||
<td>rec_r50fpn_vd_none_srn</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>NRTR</td>
|
||
<td>NRTR_MTB</td>
|
||
<td>84.21%</td>
|
||
<td>rec_mtb_nrtr</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SAR</td>
|
||
<td>Resnet31</td>
|
||
<td>87.20%</td>
|
||
<td>rec_r31_sar</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SEED</td>
|
||
<td>Aster_Resnet</td>
|
||
<td>85.35%</td>
|
||
<td>rec_resnet_stn_bilstm_att</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SVTR</td>
|
||
<td>SVTR-Tiny</td>
|
||
<td>89.25%</td>
|
||
<td>rec_svtr_tiny_none_ctc_en</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>ViTSTR</td>
|
||
<td>ViTSTR</td>
|
||
<td>79.82%</td>
|
||
<td>rec_vitstr_none_ce</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>ABINet</td>
|
||
<td>Resnet45</td>
|
||
<td>90.75%</td>
|
||
<td>rec_r45_abinet</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>VisionLAN</td>
|
||
<td>Resnet45</td>
|
||
<td>90.30%</td>
|
||
<td>rec_r45_visionlan</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SPIN</td>
|
||
<td>ResNet32</td>
|
||
<td>90.00%</td>
|
||
<td>rec_r32_gaspin_bilstm_att</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>RobustScanner</td>
|
||
<td>ResNet31</td>
|
||
<td>87.77%</td>
|
||
<td>rec_r31_robustscanner</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>RFL</td>
|
||
<td>ResNetRFL</td>
|
||
<td>88.63%</td>
|
||
<td>rec_resnet_rfl_att</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl_att_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>ParseQ</td>
|
||
<td>VIT</td>
|
||
<td>91.24%</td>
|
||
<td>rec_vit_parseq_synth</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/parseq/rec_vit_parseq_synth.tgz">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>CPPD</td>
|
||
<td>SVTR-Base</td>
|
||
<td>93.8%</td>
|
||
<td>rec_svtrnet_cppd_base_en</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/CCPD/rec_svtr_cppd_base_en_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>SATRN</td>
|
||
<td>ShallowCNN</td>
|
||
<td>88.05%</td>
|
||
<td>rec_satrn</td>
|
||
<td><a href="https://pan.baidu.com/s/10J-Bsd881bimKaclKszlaQ?pwd=lk8a">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<h3 id="13">1.3 文本超分辨率算法<a class="headerlink" href="#13" title="Permanent link">¶</a></h3>
|
||
<p>已支持的文本超分辨率算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="super_resolution/algorithm_sr_gestalt.html">Text Gestalt</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="super_resolution/algorithm_sr_telescope.html">Text Telescope</a></li>
|
||
</ul>
|
||
<p>在TextZoom公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>PSNR_Avg</th>
|
||
<th>SSIM_Avg</th>
|
||
<th>配置文件</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Text Gestalt</td>
|
||
<td>tsrn</td>
|
||
<td>19.28</td>
|
||
<td>0.6560</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/sr/sr_tsrn_transformer_strock.yml">configs/sr/sr_tsrn_transformer_strock.yml</a></td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>Text Telescope</td>
|
||
<td>tbsrn</td>
|
||
<td>21.56</td>
|
||
<td>0.7411</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/sr/sr_telescope.yml">configs/sr/sr_telescope.yml</a></td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/sr_telescope_train.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<h3 id="14">1.4 公式识别算法<a class="headerlink" href="#14" title="Permanent link">¶</a></h3>
|
||
<p>已支持的公式识别算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="formula_recognition/algorithm_rec_can.html">CAN</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="formula_recognition/algorithm_rec_latex_ocr.html">LaTeX-OCR</a></li>
|
||
</ul>
|
||
<p>在CROHME手写公式数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>配置文件</th>
|
||
<th>ExpRate</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>CAN</td>
|
||
<td>DenseNet</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/rec/rec_d28_can.yml">rec_d28_can.yml</a></td>
|
||
<td>51.72%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/contribution/rec_d28_can_train.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<h2 id="2">2. 端到端算法<a class="headerlink" href="#2" title="Permanent link">¶</a></h2>
|
||
<p>已支持的端到端OCR算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="end_to_end/algorithm_e2e_pgnet.html">PGNet</a></li>
|
||
</ul>
|
||
<h2 id="3">3. 表格识别算法<a class="headerlink" href="#3" title="Permanent link">¶</a></h2>
|
||
<p>已支持的表格识别算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="table_recognition/algorithm_table_master.html">TableMaster</a></li>
|
||
</ul>
|
||
<p>在PubTabNet表格识别公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>配置文件</th>
|
||
<th>acc</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>TableMaster</td>
|
||
<td>TableResNetExtra</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/table/table_master.yml">configs/table/table_master.yml</a></td>
|
||
<td>77.47%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar">训练模型</a> / <a href="https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar">推理模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<h2 id="4">4. 关键信息抽取算法<a class="headerlink" href="#4" title="Permanent link">¶</a></h2>
|
||
<p>已支持的关键信息抽取算法列表（戳链接获取使用教程）：</p>
|
||
<ul class="task-list">
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="kie/algorithm_kie_vi_layoutxlm.html">VI-LayoutXLM</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="kie/algorithm_kie_layoutxlm.html">LayoutLM</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="kie/algorithm_kie_layoutxlm.html">LayoutLMv2</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="kie/algorithm_kie_layoutxlm.html">LayoutXLM</a></li>
|
||
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> <a href="kie/algorithm_kie_sdmgr.html">SDMGR</a></li>
|
||
</ul>
|
||
<p>在wildreceipt发票公开数据集上，算法复现效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>配置文件</th>
|
||
<th>hmean</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>SDMGR</td>
|
||
<td>VGG6</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/sdmgr/kie_unet_sdmgr.yml">configs/kie/sdmgr/kie_unet_sdmgr.yml</a></td>
|
||
<td>86.70%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>在XFUND_zh公开数据集上，算法效果如下：</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>骨干网络</th>
|
||
<th>任务</th>
|
||
<th>配置文件</th>
|
||
<th>hmean</th>
|
||
<th>下载链接</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>VI-LayoutXLM</td>
|
||
<td>VI-LayoutXLM-base</td>
|
||
<td>SER</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh_udml.yml">ser_vi_layoutxlm_xfund_zh_udml.yml</a></td>
|
||
<td><strong>93.19%</strong></td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>LayoutXLM</td>
|
||
<td>LayoutXLM-base</td>
|
||
<td>SER</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml">ser_layoutxlm_xfund_zh.yml</a></td>
|
||
<td>90.38%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>LayoutLM</td>
|
||
<td>LayoutLM-base</td>
|
||
<td>SER</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/ser_layoutlm_xfund_zh.yml">ser_layoutlm_xfund_zh.yml</a></td>
|
||
<td>77.31%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>LayoutLMv2</td>
|
||
<td>LayoutLMv2-base</td>
|
||
<td>SER</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/ser_layoutlmv2_xfund_zh.yml">ser_layoutlmv2_xfund_zh.yml</a></td>
|
||
<td>85.44%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>VI-LayoutXLM</td>
|
||
<td>VI-LayoutXLM-base</td>
|
||
<td>RE</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/vi_layoutxlm/re_vi_layoutxlm_xfund_zh_udml.yml">re_vi_layoutxlm_xfund_zh_udml.yml</a></td>
|
||
<td><strong>83.92%</strong></td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_pretrained.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>LayoutXLM</td>
|
||
<td>LayoutXLM-base</td>
|
||
<td>RE</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml">re_layoutxlm_xfund_zh.yml</a></td>
|
||
<td>74.83%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar">训练模型</a></td>
|
||
</tr>
|
||
<tr>
|
||
<td>LayoutLMv2</td>
|
||
<td>LayoutLMv2-base</td>
|
||
<td>RE</td>
|
||
<td><a href="https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/re_layoutlmv2_xfund_zh.yml">re_layoutlmv2_xfund_zh.yml</a></td>
|
||
<td>67.77%</td>
|
||
<td><a href="https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar">训练模型</a></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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