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diff --git a/docs/about_model/index.html b/docs/about_model/index.html
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
+  <channel>
+    <title>模型相关 on RapidOCR Documentation</title>
+    <link>https://rapidai.github.io/RapidOCRDocs/docs/about_model/</link>
+    <description>Recent content in 模型相关 on RapidOCR Documentation</description>
+    <generator>Hugo -- gohugo.io</generator>
+    <language>en-us</language>
+    <lastBuildDate>Tue, 22 Nov 2022 12:36:15 +0000</lastBuildDate><atom:link href="https://rapidai.github.io/RapidOCRDocs/docs/about_model/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>不同版本模型之间比较</title>
+      <link>https://rapidai.github.io/RapidOCRDocs/docs/about_model/model_summary/</link>
+      <pubDate>Mon, 11 Sep 2023 00:00:00 +0000</pubDate>
+
+      <guid>https://rapidai.github.io/RapidOCRDocs/docs/about_model/model_summary/</guid>
+      <description>各个版本ONNX模型效果对比(仅供参考) link⚠️注意： 以下测试结果均在自己构建测试集上评测所得，不代表在其他测试集上结果也是如此。
+文本检测模型 link 评测采用的是TextDetMetric库 + 文本检测测试集，详情可以移步AI Studio运行查看。
+⚠️注意：以下表格中推理时间是基于MacBook Pro M1运行所得，不同机器会有差别，请侧重查看彼此之间的比较。
+指标计算都是在以下参数下计算得来，差别仅在于模型文件不同。
+pre_process: DetResizeForTest: limit_side_len: 736 limit_type: min NormalizeImage: std: [0.229, 0.224, 0.225] mean: [0.485, 0.456, 0.406] scale: 1./255. order: hwc ToCHWImage: KeepKeys: keep_keys: [&amp;#39;image&amp;#39;, &amp;#39;shape&amp;#39;] post_process: thresh: 0.3 box_thresh: 0.5 max_candidates: 1000 unclip_ratio: 1.6 use_dilation: true score_mode: &amp;#34;fast&amp;#34; 模型 模型大小 Precision Recall H-mean Speed(s/img) ch_PP-OCRv4_det_infer.onnx 4.5M 0.6958 0.8608 0.7696 0.6176 ch_PP-OCRv4_det_server_infer.onnx 108M 0.7070 0.9330 0.8044 13.9348 ch_PP-OCRv3_det_infer.onnx 2.3M 0.</description>
+    </item>
+
+    <item>
+      <title>支持识别语言及自助转换模型</title>
+      <link>https://rapidai.github.io/RapidOCRDocs/docs/about_model/support_language/</link>
+      <pubDate>Mon, 11 Sep 2023 00:00:00 +0000</pubDate>
+
+      <guid>https://rapidai.github.io/RapidOCRDocs/docs/about_model/support_language/</guid>
+      <description>支持识别的语言 link 因为本项目依托于PaddleOCR，所以理论上PaddleOCR支持识别的模型，RapidOCR都是支持的。 中英文检测和识别（可以直接使用） link 因为中英文是最为常用的模型，所以在打包时，就默认将中英文识别的模型放到了rapidocr_onnxruntime和rapidocr_openvino中，直接pip安装即可使用。 其他语种检测和识别（需要转换） link PaddleOCR中已有文本检测模型列表：link PaddleOCR已有文本识别模型列表： link 除了slim和蒸馏过的模型，上面链接中的其他模型都可以转换为ONNX格式，通过RapidOCR快速部署。 自助转换使用教程 link ⚠️ 主要借助paddleocr_convert库来实现。 在线快速转换 link 通过Hugging Face上的应用，快速转换模型。整体界面是下面这个样子 离线安装库转换 link 安装paddleocr_convert pip install paddleocr_convert 命令行使用 用法: $ paddleocr_convert -h usage: paddleocr_convert [-h] [-p MODEL_PATH] [-o SAVE_DIR] [-txt_path TXT_PATH] optional arguments: -h, --help show this help message and exit -p MODEL_PATH, --model_path MODEL_PATH The inference model url or local path of paddleocr. e.g. https://paddleocr.bj.bcebos.com/PP- OCRv3/chinese/ch_PP-OCRv3_det_infer.tar or models/ch_PP-OCRv3_det_infer.tar -o SAVE_DIR, --save_dir SAVE_DIR The directory of saving the model.</description>
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