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    • + @@ -521,10 +527,10 @@

    🍜2023-05-22 api update: - +
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    • + diff --git a/docs/index.xml b/docs/index.xml index 1bfeb662..36d2693f 100644 --- a/docs/index.xml +++ b/docs/index.xml @@ -32,7 +32,7 @@ Wed, 13 Sep 2023 00:00:00 +0000 https://rapidai.github.io/RapidOCRDocs/docs/related_projects/ - 相关项目 link 以下几个方向，每个都是比较独立的方向，但是却和OCR有着千丝万缕的关系，关于它们的资料就像散落在天空中的星星一般，散发着微弱的光芒，这里要做的就是将这些点点光芒聚集起来。 这里将会汇总出以下几个OCR周边项目的一些文档和资源，包括学术动态和一些工程化代码。 欢迎各位小伙伴提供PR。 版面分析 link 相关论文和帖子： 版面分析方法汇总 相关工程： PaddleOCR Layout 海康DAVAR VSR 数据集汇总： 英文版面分析数据集： + 以下几个方向，每个都是比较独立的方向，但是却和OCR有着千丝万缕的关系，关于它们的资料就像散落在天空中的星星一般，散发着微弱的光芒，这里要做的就是将这些点点光芒聚集起来。 这里将会汇总出以下几个OCR周边项目的一些文档和资源，包括学术动态和一些工程化代码。 欢迎各位小伙伴提供PR。 版面分析 link 相关论文和帖子： 版面分析方法汇总 相关工程： PaddleOCR Layout 海康DAVAR VSR 数据集汇总： 英文版面分析数据集： PubLayNet: IBM构建，34万张图像，分为5类：text, title list table figure。 DocBank：微软亚洲研究院构建，50万英文文档图像，分为12类：摘要、作者、标题、公式、图形、页脚、列表、段落、参考、节标题、表格和文章标题。 中文版面分析数据集： CDLA：中文文档版面分析数据集，面向中文文献类（论文）场景，总共6000张（5000训练，1000测试），分为10类：正文、标题、图片、图片标题、表格、表格标题、页眉、页脚、注释和公式。 表格识别 link 相关论文和帖子： OCR之表格结构识别综述 合合信息：表格识别与内容提炼技术理解及研发趋势 论文阅读: （ICDAR2021 海康威视）LGPMA（表格识别算法）及官方源码对应解读 相关工程： 海康官方LGPMA源码 LGPMA Inference PaddleOCR Table 数据集汇总： 英文表格识别数据集： PubTabNet: IBM构建，568k+文档图像数据，包括表格图像和对应的HTML标注。 视频OCR link 相关论文和帖子： 【NeurIPS2021】A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer | 博客解读 【ACM MM 2019】You only recognize once: Towards fast video text spotting 相关工程： video-subtitle-extractor: 一款将视频中的硬字幕提取为外挂字幕文件(srt格式)的软件 RapidVideOCR: 提取视频中硬字幕 数据集汇总： BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting: 快手科技、浙江大学和北京邮电大学合作提出，大规模双语开放场景下的视频文本基准数据集，该数据集主要提供了2000+视频，1,750,000帧开放视频场景的视频。同时，还提供了丰富的标注类型（标题、字幕、场景文本等）。该数据集支持四个任务：视频帧检测、视频帧识别、视频文本跟踪和端到端视频文本识别。 卡证OCR link 相关论文和帖子： 相关工程： fake_certificate_generator: 假的证件合成器，包括身份证、驾驶证、营业执照。 数据集汇总： 暂无，一般这类数据较为敏感，通常都合成假数据来使用。 印章OCR link 相关论文和帖子： 【技术新趋势】合合信息：复杂环境下ocr与印章识别技术理解及研发趋势 基于文字分割的印章识别技术 相关工程： JS生成印章 Python绘制透明背景印章 在线印章合成大全网站 数据集汇总： 暂无 diff --git a/docs/inference_engine/index.html b/docs/inference_engine/index.html index d5c87348..f3faa020 100644 --- a/docs/inference_engine/index.html +++ b/docs/inference_engine/index.html @@ -126,6 +126,12 @@
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    Global部分

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    参数名称取值范围默认值作用
    text_score[0, 1]0.5文本识别结果置信度，值越大，把握越大
    use_angle_clsbooltrue是否使用文本行的方向分类
    print_verbosebooltrue是否打印各个部分耗时信息
    min_heightint30图像最小高度（单位是像素）
    低于这个值，会跳过文本检测阶段，直接进行后续识别
    width_height_ratioint8如果输入图像的宽高比大于width_height_ratio，则会跳过文本检测，直接进行后续识别
    width_height_ratio=-1：不用这个参数
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      min_height是用来过滤只有一行文本的图像（如下图），这类图像不会进入文本检测模块，直接进入后续过程。

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    参数名称取值范围默认值作用
    use_cudaboolfalse是否使用CUDA，加速推理
    limit_side_len-736限制图像边的长度的像素值
    limit_type[min, max]min限制图像的最小边长度还是最大边为limit_side_len
    示例解释：当limit_type=min和limit_side_len=736时，图像最小边小于736时，
    会将图像最小边拉伸到736，另一边则按图像原始比例等比缩放。
    thresh[0, 1]0.3图像中文字部分和背景部分分割阈值
    值越大，文字部分会越小
    box_thresh[0, 1]0.5文本检测所得框是否保留的阈值，值越大，召回率越低
    max_candidates-1000图像中最大可检测到的文本框数目，一般够用
    unclip_ratio[1.6, 2.0]1.6控制文本检测框的大小，值越大，检测框整体越大
    use_dilationbooltrue是否使用形态学中的膨胀操作，一般采用默认值即可
    score_modestringfastfast是求rectangle区域的平均分数，容易造成弯曲文本漏检，slow是求polygon区域的平均分数，会更准确，但速度有所降低，可按需选择
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    参数名称取值范围默认值作用
    cls_img_shape-[3, 48, 192]输入方向分类模型的图像Shape（CHW）
    cls_batch_num-6批次推理的batch大小，一般采用默认值即可，太大并没有明显提速，效果还可能会差
    cls_thresh[0, 1]0.9方向分类结果的置信度
    label_list-[0, 180]方向分类的标签，0°或者180°，该参数不能动
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    Rec部分

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    参数名称取值范围默认值作用
    rec_img_shape-[3, 48, 320]输入文本识别模型的图像Shape（CHW）
    rec_batch_num-6批次推理的batch大小，一般采用默认值即可，太大并没有明显提速，效果还可能会差
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    + + + + diff --git a/docs/packages/rapidocr/index.xml b/docs/packages/rapidocr/index.xml index 7f79157f..8e2a51c1 100644 --- a/docs/packages/rapidocr/index.xml +++ b/docs/packages/rapidocr/index.xml @@ -13,12 +13,12 @@ Mon, 11 Sep 2023 00:00:00 +0000 https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/rapidocr_onnxruntime/ - RapidOCR Python link 简介 link 该部分为python版的OCR推理，包括基于ONNXRuntime和OpenVINO两个推理引擎的包。 因为模型较小，因此将相关模型都已打到Whl包，可直接pip安装使用。 说明 link rapidocr_onnxruntime和rapidocr_openvino两个包除推理引擎不同之外，其余均相同，默认都是在CPU上运行。 按语义版本号来讲，两个包其主版本号和次版本号同步更新，修订号可能会有区别。也就是说x.y.z中x和y是一样的，z可能不同。 各个版本的对应关系： 版本 内置模型版本 对应PaddleOCR 分支 v1.3.x PaddleOCR v4版 release/2.7 v1.2.xv1.1.xv1.0.x PaddleOCR v3版 release/2.6 推荐使用PaddleOCRModelConveter在线转换，现用现转。推理代码都是同一个，只需更换模型即可。已转好ONNX模型下载地址：百度网盘 | Google Drive，大家可自行搭配使用。 所有常用的参数配置都在config.yaml下，其中每个独立的模块下均有独立的config.yaml配置文件，可以单独使用。 关于选择哪个版本的包（rapidocr_onnxruntime 或者 rapidocr_openvino）? 推理引擎 推理速度更快 占用内存更少 rapidocr_onnxruntime ✓ rapidocr_openvino ✓ ⚠️ openvino存在内存不释放的问题，参见wiki pip安装快速使用（推荐） link 安装环境 + 简介 link 该部分为python版的OCR推理，包括基于ONNXRuntime和OpenVINO两个推理引擎的包。 因为模型较小，因此将相关模型都已打到Whl包，可直接pip安装使用。 说明 link rapidocr_onnxruntime和rapidocr_openvino两个包除推理引擎不同之外，其余均相同，默认都是在CPU上运行。 按语义版本号来讲，两个包其主版本号和次版本号同步更新，修订号可能会有区别。也就是说x.y.z中x和y是一样的，z可能不同。 各个版本的对应关系： 版本 内置模型版本 对应PaddleOCR 分支 v1.3.x PaddleOCR v4版 release/2.7 v1.2.xv1.1.xv1.0.x PaddleOCR v3版 release/2.6 推荐使用PaddleOCRModelConveter在线转换，现用现转。推理代码都是同一个，只需更换模型即可。已转好ONNX模型下载地址：百度网盘 | Google Drive，大家可自行搭配使用。 所有常用的参数配置都在config.yaml下，其中每个独立的模块下均有独立的config.yaml配置文件，可以单独使用。 关于选择哪个版本的包（rapidocr_onnxruntime 或者 rapidocr_openvino）? 推理引擎 推理速度更快 占用内存更少 rapidocr_onnxruntime ✓ rapidocr_openvino ✓ ⚠️ openvino存在内存不释放的问题，参见wiki pip安装快速使用（推荐） link 安装环境 包名 版本 安装命令 rapidocr_onnxruntime pip install rapidocr-onnxruntime rapidocr_openvino pip install rapidocr-openvino python脚本使用 ⚠️注意：初始化RapidOCR可不提供config.yaml，默认使用安装目录下的config.yaml。如有自定义需求： 一是可直接通过初始化参数传入。详细参数参考下面命令行部分，和config.yaml基本对应。 二是复制config.yaml，自行更改，然后初始化给出。e.g. engine = RapidOCR(config_path=&quot;custom.yaml&quot;) 输入：Union[str, np.ndarray, bytes, Path] 输出： -有值：([[文本框坐标], 文本内容, 置信度], 推理时间)，e. +有值：([[文本框坐标], 文本内容, 置信度], 推理时间)，e.g. [[左上, 右上, 右下, 左下], '小明', '0. @@ -27,12 +27,33 @@ Mon, 11 Sep 2023 00:00:00 +0000 https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/rapidocr_openvino/ - RapidOCR Python link 简介 link 该部分为python版的OCR推理，包括基于ONNXRuntime和OpenVINO两个推理引擎的包。 因为模型较小，因此将相关模型都已打到Whl包，可直接pip安装使用。 说明 link rapidocr_onnxruntime和rapidocr_openvino两个包除推理引擎不同之外，其余均相同，默认都是在CPU上运行。 按语义版本号来讲，两个包其主版本号和次版本号同步更新，修订号可能会有区别。也就是说x.y.z中x和y是一样的，z可能不同。 各个版本的对应关系： 版本 内置模型版本 对应PaddleOCR 分支 v1.3.x PaddleOCR v4版 release/2.7 v1.2.xv1.1.xv1.0.x PaddleOCR v3版 release/2.6 推荐使用PaddleOCRModelConveter在线转换，现用现转。推理代码都是同一个，只需更换模型即可。已转好ONNX模型下载地址：百度网盘 | Google Drive，大家可自行搭配使用。 所有常用的参数配置都在config.yaml下，其中每个独立的模块下均有独立的config.yaml配置文件，可以单独使用。 关于选择哪个版本的包（rapidocr_onnxruntime 或者 rapidocr_openvino）? 推理引擎 推理速度更快 占用内存更少 rapidocr_onnxruntime ✓ rapidocr_openvino ✓ ⚠️ openvino存在内存不释放的问题，参见wiki pip安装快速使用（推荐） link 安装环境 + 简介 link 该部分为python版的OCR推理，包括基于ONNXRuntime和OpenVINO两个推理引擎的包。 因为模型较小，因此将相关模型都已打到Whl包，可直接pip安装使用。 说明 link rapidocr_onnxruntime和rapidocr_openvino两个包除推理引擎不同之外，其余均相同，默认都是在CPU上运行。 按语义版本号来讲，两个包其主版本号和次版本号同步更新，修订号可能会有区别。也就是说x.y.z中x和y是一样的，z可能不同。 各个版本的对应关系： 版本 内置模型版本 对应PaddleOCR 分支 v1.3.x PaddleOCR v4版 release/2.7 v1.2.xv1.1.xv1.0.x PaddleOCR v3版 release/2.6 推荐使用PaddleOCRModelConveter在线转换，现用现转。推理代码都是同一个，只需更换模型即可。已转好ONNX模型下载地址：百度网盘 | Google Drive，大家可自行搭配使用。 所有常用的参数配置都在config.yaml下，其中每个独立的模块下均有独立的config.yaml配置文件，可以单独使用。 关于选择哪个版本的包（rapidocr_onnxruntime 或者 rapidocr_openvino）? 推理引擎 推理速度更快 占用内存更少 rapidocr_onnxruntime ✓ rapidocr_openvino ✓ ⚠️ openvino存在内存不释放的问题，参见wiki pip安装快速使用（推荐） link 安装环境 包名 版本 安装命令 rapidocr_onnxruntime pip install rapidocr-onnxruntime rapidocr_openvino pip install rapidocr-openvino python脚本使用 ⚠️注意：初始化RapidOCR可不提供config.yaml，默认使用安装目录下的config.yaml。如有自定义需求： 一是可直接通过初始化参数传入。详细参数参考下面命令行部分，和config.yaml基本对应。 二是复制config.yaml，自行更改，然后初始化给出。e.g. engine = RapidOCR(config_path=&quot;custom.yaml&quot;) 输入：Union[str, np.ndarray, bytes, Path] 输出： -有值：([[文本框坐标], 文本内容, 置信度], 推理时间)，e. +有值：([[文本框坐标], 文本内容, 置信度], 推理时间)，e.g. [[左上, 右上, 右下, 左下], '小明', '0. + + + + RapidOCR调优尝试教程 + https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/optim_tutorial/ + Mon, 11 Sep 2023 00:00:00 +0000 + + https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/optim_tutorial/ + 引言 link 由于小伙伴们使用OCR的场景多种多样，单一的参数配置往往不能满足要求，这就涉及到基于现有模型，通过调节参数来达到正确识别的目的。 基本的原则是尽量不去动模型内部，降低使用成本。 本篇文章就来说说在识别效果不好时，如何去调优。 因此博客为一家之言，难免会挂一漏万，希望小伙伴多多指出，共同维护这份教程。 ✨以下尝试情况，均已经在在线demo中实现，看完之后，可移步那里去尝试。 常见错例种类 link本地使用过程中，识别结果： +个别字丢失 个别字识别错误 个别字丢失调优篇 link 查看图像是否shape太小，导致文本检测丢失。 +对于长度较长，高度较小的图像，可尝试对该图像高度做上下补充，来减小文字在原始图像中比例，利于文本检测。类似下图这种，左侧一条为原始图像，右侧为上下补边的图像。 padding代码参考： from typing import Tuple import cv2 import numpy as np def padding_img(img: np.ndarray, padding_value: Tuple[int, int, int, int], padding_color: Tuple = (0, 0, 0)) -&gt; np.ndarray: padded_img = cv2.copyMakeBorder(img, padding_value[0], padding_value[1], padding_value[2], padding_value[3], cv2.BORDER_CONSTANT, value=padding_color) return padded_img img = cv2.imread(&#39;xxx.jpg&#39;) # padding_value: (top, bottom, left, right) # 对该图像上下各补充10像素的白边 padded_img = padding_img(img, (10, 10, 0, 0), (255, 255, 255)) 将padding之后的图像再次送入RapidOCR中尝试，查看是否仍然出现上述问题。 将该图像上传到在线demo中，查看能否复现问题？ + + + + config.yaml参数解释 + https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/config_parameter/ + Mon, 11 Sep 2023 00:00:00 +0000 + + https://rapidai.github.io/RapidOCRDocs/docs/packages/rapidocr/config_parameter/ + Global部分 link 参数名称 取值范围 默认值 作用 text_score [0, 1] 0.5 文本识别结果置信度，值越大，把握越大 use_angle_cls bool true 是否使用文本行的方向分类 print_verbose bool true 是否打印各个部分耗时信息 min_height int 30 图像最小高度（单位是像素）低于这个值，会跳过文本检测阶段，直接进行后续识别 width_height_ratio int 8 如果输入图像的宽高比大于width_height_ratio，则会跳过文本检测，直接进行后续识别width_height_ratio=-1：不用这个参数 min_height是用来过滤只有一行文本的图像（如下图），这类图像不会进入文本检测模块，直接进入后续过程。 +Det部分 link 参数名称 取值范围 默认值 作用 use_cuda bool false 是否使用CUDA，加速推理 limit_side_len - 736 限制图像边的长度的像素值 limit_type [min, max] min 限制图像的最小边长度还是最大边为limit_side_len 示例解释：当limit_type=min和limit_side_len=736时，图像最小边小于736时，会将图像最小边拉伸到736，另一边则按图像原始比例等比缩放。 thresh [0, 1] 0.3 图像中文字部分和背景部分分割阈值值越大，文字部分会越小 box_thresh [0, 1] 0.5 文本检测所得框是否保留的阈值，值越大，召回率越低 max_candidates - 1000 图像中最大可检测到的文本框数目，一般够用 unclip_ratio [1.6, 2.0] 1.6 控制文本检测框的大小，值越大，检测框整体越大 use_dilation bool true 是否使用形态学中的膨胀操作，一般采用默认值即可 score_mode string fast fast是求rectangle区域的平均分数，容易造成弯曲文本漏检，slow是求polygon区域的平均分数，会更准确，但速度有所降低，可按需选择 Cls部分 link 参数名称 取值范围 默认值 作用 cls_img_shape - [3, 48, 192] 输入方向分类模型的图像Shape（CHW） cls_batch_num - 6 批次推理的batch大小，一般采用默认值即可，太大并没有明显提速，效果还可能会差 cls_thresh [0, 1] 0. diff --git a/docs/packages/rapidocr/optim_tutorial/index.html b/docs/packages/rapidocr/optim_tutorial/index.html new file mode 100644 index 00000000..b9575041 --- /dev/null +++ b/docs/packages/rapidocr/optim_tutorial/index.html @@ -0,0 +1,770 @@ + + + + + + + + + + + + + + + + + + + + + RapidOCR调优尝试教程 | RapidOCR Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +
    On this page
    + +
    + + +
    + +
    +
    +
    + +

    + + RapidOCR调优尝试教程 + + +

    +
    + +

    + +
    + + +
    +

    引言

      +
    • 由于小伙伴们使用OCR的场景多种多样，单一的参数配置往往不能满足要求，这就涉及到基于现有模型，通过调节参数来达到正确识别的目的。
      • +
    • 基本的原则是尽量不去动模型内部，降低使用成本。
      • +
    • 本篇文章就来说说在识别效果不好时，如何去调优。
      • +
    • 因此博客为一家之言，难免会挂一漏万，希望小伙伴多多指出，共同维护这份教程。
      • +
    • ✨以下尝试情况，均已经在在线demo中实现，看完之后，可移步那里去尝试。
      • +
    +

    常见错例种类

    本地使用过程中，识别结果：

    +
      +
    • 个别字丢失
      • +
    • 个别字识别错误
      • +
    +

    个别字丢失调优篇

      +
    1. +

      查看图像是否shape太小，导致文本检测丢失。

      +
        +
      • 对于长度较长，高度较小的图像，可尝试对该图像高度做上下补充，来减小文字在原始图像中比例，利于文本检测。类似下图这种，左侧一条为原始图像，右侧为上下补边的图像。 + + + + + + + + + + + + + Snipaste_2023-04-09_14-01-45 + + +
        • +
      • padding代码参考：
        • +
      + + + + + + + + + + + + + + +
      + 
      +  from typing import Tuple
+
+import cv2
+import numpy as np
+
+
+def padding_img(img: np.ndarray,
+                padding_value: Tuple[int, int, int, int],
+                padding_color: Tuple = (0, 0, 0)) -> np.ndarray:
+    padded_img = cv2.copyMakeBorder(img,
+                                    padding_value[0],
+                                    padding_value[1],
+                                    padding_value[2],
+                                    padding_value[3],
+                                    cv2.BORDER_CONSTANT,
+                                    value=padding_color)
+    return padded_img
+
+img = cv2.imread('xxx.jpg')
+
+# padding_value: (top, bottom, left, right)
+# 对该图像上下各补充10像素的白边
+padded_img = padding_img(img, (10, 10, 0, 0), (255, 255, 255))
+
      +
      +
        +
      • 将padding之后的图像再次送入RapidOCR中尝试，查看是否仍然出现上述问题。
        • +
      +
      2. +
    2. +

      将该图像上传到在线demo中，查看能否复现问题？

      +
        +
      • 如不能复现，着重比对环境是否与在线demo中相关包使用版本不一致情况？
        • +
      • 在线demo所用的各个包版本情况： + + + + + + + + + + + + + + +
        + 
        +  Python: 3.8
+onnxruntime: 1.14.1
+rapidocr_onnxruntime: 1.2.5
+
        +
        +
        • +
      +
      3. +
    3. +

      如能复现，尝试调节在线demo中的三个参数，注意控制变量调节参数

      +
        +
      1. 首先调节box_thresh参数，该参数用来控制文本检测部分检测所得框是文本的概率。个别字丢失，一般是单独的字丢失，很大可能是文本检测模型没有检测到该独立的文字。尝试调低box_thresh值，查看是否可以检出丢失的文字。（没有找到合适的例子）
        2. +
      2. 固定box_thresh，调节text_score参数。该参数是控制识别文本结果正确的概率。尝试调低text_score值，查看是否可以识别出丢失的文字。
        3. +
      3. 固定box_thresh和text_score两个参数，调节unclip_ratio参数。该参数用来控制文本检测所得文本框的大小。尝试调大unclip_ratio值，查看是否可以识别出丢失的文字。
        4. +
      +
      4. +
    4. +

      如果调节三个参数也不能解决问题，只能尝试server版的文本检测和文本识别模型了。

      +
        +
      1. 首先更换server版的文本检测模型，查看是否解决问题
        2. +
      2. 在第1步基础上，更换server版的文本识别模型，查看是否解决问题
        3. +
      +
      5. +
    5. +

      尝试更换不同版本模型，来查看效果。具体可在在线demo中尝试

      +
      6. +
    +

    个别字识别错误调优篇

      +
    • 个别字错误的情况，例如：成识别为戍，这种一般是文本识别模型的问题。
      • +
    +

    情况一：轻量中英文模型识别对个别汉字识别错误

      +
    1. 尝试padding图像，再重新识别
      2. +
    2. 更换不同版本的识别模型，包括v2、v3和server版尝试
      3. +
    +

    情况二：轻量中英文模型对个别英文或数字识别错误

      +
    1. 更换不同版本的识别模型，包括v2、v3和server版尝试
      2. +
    2. 如果是只识别英文单词和数字，可以更换英文和数字专有模型（en_PP-OCRv3_rec_infer.onnx和en_number_mobile_v2.0_rec_infer.onnx）
      3. +
    + +
    + + + + + + + + + + + + + + + +
    +
    + + + + + + + + + + + Edit this page + + +
    + +
    +

    Last updated + 14 Sep 2023, 22:24 +0800 + . history +

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    + + © 2023 SWHL. Built with Lotus Docs +

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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/packages/rapidocr/rapidocr_onnxruntime/index.html b/docs/packages/rapidocr/rapidocr_onnxruntime/index.html index 18a39c14..fc6cd780 100644 --- a/docs/packages/rapidocr/rapidocr_onnxruntime/index.html +++ b/docs/packages/rapidocr/rapidocr_onnxruntime/index.html @@ -126,6 +126,12 @@
  • rapidocr_openvino
    • + +
  • RapidOCR调优尝试教程
    • + + +
  • config.yaml参数解释
    • + @@ -405,7 +411,7 @@