introduction_cn.ipynb

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    "## 1. PP-OCRv3模型简介\n",
    "\n",
    "PP-OCRv3在PP-OCRv2的基础上进一步升级。整体的框架图保持了与PP-OCRv2相同的pipeline，针对检测模型和识别模型进行了优化。其中，检测模块仍基于DB算法优化，而识别模块不再采用CRNN，换成了IJCAI 2022最新收录的文本识别算法SVTR，并对其进行产业适配。PP-OCRv3系统框图如下所示（粉色框中为PP-OCRv3新增策略）：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocrv3_framework.png\"  width = \"80%\"  />\n",
    "</div>\n",
    "\n",
    "\n",
    "从算法改进思路上看，分别针对检测和识别模型，进行了共9个方面的改进：\n",
    "\n",
    "- 检测模块：\n",
    "    - LK-PAN：大感受野的PAN结构；\n",
    "    - DML：教师模型互学习策略；\n",
    "    - RSE-FPN：残差注意力机制的FPN结构；\n",
    "\n",
    "\n",
    "- 识别模块：\n",
    "    - SVTR_LCNet：轻量级文本识别网络；\n",
    "    - GTC：Attention指导CTC训练策略；\n",
    "    - TextConAug：挖掘文字上下文信息的数据增广策略；\n",
    "    - TextRotNet：自监督的预训练模型；\n",
    "    - UDML：联合互学习策略；\n",
    "    - UIM：无标注数据挖掘方案。\n",
    "\n",
    "从效果上看，速度可比情况下，多种场景精度均有大幅提升：\n",
    "- 中文场景，相对于PP-OCRv2中文模型提升超5%；\n",
    "- 英文数字场景，相比于PP-OCRv2英文模型提升11%；\n",
    "- 多语言场景，优化80+语种识别效果，平均准确率提升超5%。\n",
    "\n",
    "\n",
    "更详细的优化细节可参考技术报告：https://arxiv.org/abs/2206.03001 。\n",
    "\n",
    "更多关于PaddleOCR的内容，可以点击 https://github.com/PaddlePaddle/PaddleOCR 进行了解。\n",
    "\n",
    "\n"
   ]
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   "source": [
    "## 2. 模型效果\n",
    "\n",
    "PP-OCRv3的效果如下：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12406017/200261622-1b928d93-93ab-4575-8c60-214bcc03eda1.png\"  width = \"80%\"  />\n",
    "</div>\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12406017/200261711-9f18bb04-3736-4f51-892c-de801db9ab9e.png\"  width = \"80%\"  />\n",
    "</div>\n",
    "\n",
    "\n",
    "\n"
   ]
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   "source": [
    "## 3. 模型如何使用\n",
    "\n",
    "### 3.1 模型推理\n",
    "* 安装PaddleOCR whl包"
   ]
  },
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   "source": [
    "! pip install paddleocr --user"
   ]
  },
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   "source": [
    "* 快速体验"
   ]
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   "source": [
    "# 命令行使用\n",
    "! wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/imgs/11.jpg\n",
    "! paddleocr --image_dir 11.jpg --use_angle_cls true"
   ]
  },
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    "运行完成后，会在终端输出如下结果：\n",
    "```log\n",
    "[[[28.0, 37.0], [302.0, 39.0], [302.0, 72.0], [27.0, 70.0]], ('纯臻营养护发素', 0.96588134765625)]\n",
    "[[[26.0, 81.0], [172.0, 83.0], [172.0, 104.0], [25.0, 101.0]], ('产品信息/参数', 0.9113278985023499)]\n",
    "[[[28.0, 115.0], [330.0, 115.0], [330.0, 132.0], [28.0, 132.0]], ('（45元/每公斤，100公斤起订）', 0.8843421936035156)]\n",
    "......\n",
    "```\n",
    "\n",
    "\n"
   ]
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   "source": [
    "### 3.2 模型训练\n",
    "PP-OCR系统由文本检测模型、方向分类器和文本识别模型构成，三个模型训练教程可参考如下文档:\n",
    "1. 文本检测模型：[文本检测训练教程](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/detection.md)\n",
    "1. 方向分类器: [方向分类器训练教程](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/angle_class.md)\n",
    "1. 文本识别模型：[文本识别训练教程](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/recognition.md)\n",
    "\n",
    "模型训练完成后，可以通过指定模型路径的方式串联使用\n",
    "命令参考如下：\n",
    "```python\n",
    "paddleocr --image_dir 11.jpg --use_angle_cls true --det_model_dir=/path/to/det_inference_model --cls_model_dir=/path/to/cls_inference_model --rec_model_dir=/path/to/rec_inference_model\n",
    "```"
   ]
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   "source": [
    "## 4. 原理\n",
    "\n",
    "优化思路具体如下\n",
    "\n",
    "1. 检测模型优化\n",
    "- LK-PAN：大感受野的PAN结构。\n",
    "  \n",
    "LK-PAN (Large Kernel PAN) 是一个具有更大感受野的轻量级PAN结构，核心是将PAN结构的path augmentation中卷积核从3*3改为9*9。通过增大卷积核，提升特征图每个位置覆盖的感受野，更容易检测大字体的文字以及极端长宽比的文字。使用LK-PAN结构，可以将教师模型的hmean从83.2%提升到85.0%。\n",
    "     <div align=\"center\">\n",
    "   <img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocr_v3/LKPAN.png\"  width = \"60%\"  />\n",
    "   </div>\n",
    "\n",
    "- DML：教师模型互学习策略\n",
    "\n",
    "[DML](https://arxiv.org/abs/1706.00384) （Deep Mutual Learning）互学习蒸馏方法，如下图所示，通过两个结构相同的模型互相学习，可以有效提升文本检测模型的精度。教师模型采用DML策略，hmean从85%提升到86%。将PP-OCRv2中CML的教师模型更新为上述更高精度的教师模型，学生模型的hmean可以进一步从83.2%提升到84.3%。\n",
    "   <div align=\"center\">\n",
    "   <img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocr_v3/teacher_dml.png\"  width = \"60%\"  />\n",
    "   </div>\n",
    "\n",
    "- RSE-FPN：残差注意力机制的FPN结构\n",
    "\n",
    "RSE-FPN（Residual Squeeze-and-Excitation FPN）如下图所示，引入残差结构和通道注意力结构，将FPN中的卷积层更换为通道注意力结构的RSEConv层，进一步提升特征图的表征能力。考虑到PP-OCRv2的检测模型中FPN通道数非常小，仅为96，如果直接用SEblock代替FPN中卷积会导致某些通道的特征被抑制，精度会下降。RSEConv引入残差结构会缓解上述问题，提升文本检测效果。进一步将PP-OCRv2中CML的学生模型的FPN结构更新为RSE-FPN，学生模型的hmean可以进一步从84.3%提升到85.4%。\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocr_v3/RSEFPN.png\"  width = \"60%\"  />\n",
    "</div>\n",
    "\n",
    "1. 识别模型优化\n",
    "- SVTR_LCNet：轻量级文本识别网络\n",
    "\n",
    "SVTR_LCNet是针对文本识别任务，将基于Transformer的SVTR网络和轻量级CNN网络PP-LCNet 融合的一种轻量级文本识别网络。使用该网络，预测速度优于PP-OCRv2的识别模型20%，但是由于没有采用蒸馏策略，该识别模型效果略差。此外，进一步将输入图片规范化高度从32提升到48，预测速度稍微变慢，但是模型效果大幅提升，识别准确率达到73.98%（+2.08%），接近PP-OCRv2采用蒸馏策略的识别模型效果。\n",
    "\n",
    "- GTC：Attention指导CTC训练策略\n",
    "  \n",
    "[GTC](https://arxiv.org/pdf/2002.01276.pdf)（Guided Training of CTC），利用Attention模块CTC训练，融合多种文本特征的表达，是一种有效的提升文本识别的策略。使用该策略，预测时完全去除 Attention 模块，在推理阶段不增加任何耗时，识别模型的准确率进一步提升到75.8%（+1.82%）。训练流程如下所示：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12406017/200265540-1bbb730f-35d4-4d72-8e00-70856bb932ee.png\"  width = \"60%\"  />\n",
    "</div>\n",
    "\n",
    "- TextConAug：挖掘文字上下文信息的数据增广策略\n",
    "\n",
    "TextConAug是一种挖掘文字上下文信息的数据增广策略，主要思想来源于论文[ConCLR](https://www.cse.cuhk.edu.hk/~byu/papers/C139-AAAI2022-ConCLR.pdf)，作者提出ConAug数据增广，在一个batch内对2张不同的图像进行联结，组成新的图像并进行自监督对比学习。PP-OCRv3将此方法应用到有监督的学习任务中，设计了TextConAug数据增强方法，可以丰富训练数据上下文信息，提升训练数据多样性。使用该策略，识别模型的准确率进一步提升到76.3%（+0.5%）。TextConAug示意图如下所示：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12406017/200265540-1bbb730f-35d4-4d72-8e00-70856bb932ee.png\"  width = \"60%\"  />\n",
    "</div>\n",
    "\n",
    "- TextRotNet：自监督的预训练模型\n",
    "\n",
    "TextRotNet是使用大量无标注的文本行数据，通过自监督方式训练的预训练模型，参考于论文[STR-Fewer-Labels](https://github.com/ku21fan/STR-Fewer-Labels)。该模型可以初始化SVTR_LCNet的初始权重，从而帮助文本识别模型收敛到更佳位置。使用该策略，识别模型的准确率进一步提升到76.9%（+0.6%）。TextRotNet训练流程如下图所示：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocr_v3/SSL.png\"  width = \"60%\"  />\n",
    "</div>\n",
    "\n",
    "- UDML：联合互学习策略\n",
    "\n",
    "UDML（Unified-Deep Mutual Learning）联合互学习是PP-OCRv2中就采用的对于文本识别非常有效的提升模型效果的策略。在PP-OCRv3中，针对两个不同的SVTR_LCNet和Attention结构，对他们之间的PP-LCNet的特征图、SVTR模块的输出和Attention模块的输出同时进行监督训练。使用该策略，识别模型的准确率进一步提升到78.4%（+1.5%）。\n",
    "\n",
    "- UDML：联合互学习策略\n",
    "\n",
    "UIM（Unlabeled Images Mining）是一种非常简单的无标注数据挖掘方案。核心思想是利用高精度的文本识别大模型对无标注数据进行预测，获取伪标签，并且选择预测置信度高的样本作为训练数据，用于训练小模型。使用该策略，识别模型的准确率进一步提升到79.4%（+1%）。\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/ppocr_v3/UIM.png\"  width = \"60%\"  />\n",
    "</div>"
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   "source": [
    "## 5. 注意事项\n",
    "\n",
    "PP-OCR系列模型训练过程中均使用通用数据，如在实际场景中表现不满意，可标注少量数据进行finetune。"
   ]
  },
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   "source": [
    "## 6. 相关论文以及引用信息\n",
    "```\n",
    "@article{li2022pp,\n",
    "  title={PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System},\n",
    "  author={Li, Chenxia and Liu, Weiwei and Guo, Ruoyu and Yin, Xiaoting and Jiang, Kaitao and Du, Yongkun and Du, Yuning and Zhu, Lingfeng and Lai, Baohua and Hu, Xiaoguang and others},\n",
    "  journal={arXiv preprint arXiv:2206.03001},\n",
    "  year={2022}\n",
    "}\n",
    "```\n"
   ]
  }
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