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README
Apache-2.0

OcrLiteOnnx

联系方式

  • QQ①群:994929053(满)
  • QQ②群:820132154(满)
  • QQ③群:904091319(满)
  • QQ④群:615877948(满)
  • QQ⑤群:185905924(满)
  • QQ⑥群:628010752

Project下载

  • 整合好源码和依赖库的完整工程项目,可以在Release中下载(github)
  • 可到Q群共享内下载,以Project开头的压缩包文件为源码工程,例:Project_OcrLiteOnnx-版本号.7z
  • 如果想自己折腾,则请继续阅读本说明

Demo下载(win、mac、linux)

  • 编译好的demo,可以在release中下载,或者Q群共享内下载
  • 或者Gitee下载
  • 或者Github下载
  • 各平台可执行文件:linux-bin.7z、macos-bin.7z、windows-bin.7z
  • 用于java的jni库:linux-jni.7z、macos-jni.7z、windows-jni.7z
  • 用于C的动态库:linux-clib.7z、macos-clib.7z、windows-clib.7z
  • C动态库调用范例:OcrLiteOnnxLibTest
  • 注意:linux编译平台为ubuntu18.04,如果你的linux版本无法运行demo,请自行从源码编译依赖库和完整项目。

介绍

ChineseOcr Lite Onnx,超轻量级中文OCR PC Demo,支持onnxruntime推理

对应chineseocr lite的onnx分支

这个项目使用onnxruntime框架进行推理

详情请查看 https://github.com/ouyanghuiyu/chineseocr_lite

采用onnxruntime框架https://github.com/microsoft/onnxruntime

更新说明

2021-10-15 update

  • opencv 4.5.4
  • onnxruntime 1.9.0
  • 优化编译脚本和测试脚本

2021-10-25 update

  • fix:win编译打包C动态库缺少lib文件
  • 编译c动态库添加打包include

2022-06-18 update

  • opencv 4.6.0
  • onnxruntime 1.11.1
  • 修改c lib导出方法,支持C调用

2022-10-14 update

  • onnxruntime 1.12.1
  • 支持mt版引用库

2023-02-13 update

  • onnxruntime 1.14.0

模型下载

模型下载地址 下载后解压到项目根目录

OcrLiteOnnx/models
    ├── angle_net.onnx
    ├── crnn_lite_lstm.onnx
    ├── dbnet.onnx
    └── keys.txt

编译说明

测试说明

  1. 根据系统下载对应的程序包linux-bin.7z、macos-bin.7z、windows-bin.7z,并解压.
  2. 把上面的模型下载,解压到第一步解压的文件夹里.
  3. 终端运行run-test.sh或命令行运行run-test.bat,查看识别结果.
  4. 终端运行run-benchmark.sh或命令行运行run-benchmark.bat,查看识别过程平均耗时.

FAQ

windows静态链接msvc

  • 作用:静态链接CRT(mt)可以让编译出来的包,部署时不需要安装c++运行时,但会增大包体积;
  • 需要mt版的引用库,参考编译说明,下载mt版的库;

macOS缺少openmp(从1.7.0开始已不再依赖openmp)

brew install libomp

windows提示缺少"VCRUNTIME140_1.dll"

下载安装适用于 Visual Studio 2015、2017 和 2019 的 Microsoft Visual C++ 可再发行软件包 下载地址

Windows7执行错误|中文乱码

  1. cmd窗口左上角-属性
  2. 字体选项卡-选择除了“点阵字体”以外的TrueType字体,例如:Lucida Console、宋体
  3. 重新执行bat

输入参数说明

  • 请参考main.h中的命令行参数说明。
  • 每个参数有一个短参数名和一个长参数名,用短的或长的均可。
  1. -d或--models:模型所在文件夹路径,可以相对路径也可以绝对路径。
  2. -1或--det:dbNet模型文件名(含扩展名)
  3. -2或--cls:angleNet模型文件名(含扩展名)
  4. -3或--rec:crnnNet模型文件名(含扩展名)
  5. -4或--keys:keys.txt文件名(含扩展名)
  6. -i或--image:目标图片路径,可以相对路径也可以绝对路径。
  7. -t或--numThread:线程数量。
  8. -p或--padding:图像预处理,在图片外周添加白边,用于提升识别率,文字框没有正确框住所有文字时,增加此值。
  9. -s或--maxSideLen :按图片最长边的长度,此值为0代表不缩放,例:1024,如果图片长边大于1024则把图像整体缩小到1024再进行图像分割计算,如果图片长边小于1024则不缩放,如果图片长边小于32,则缩放到32。
  10. -b或--boxScoreThresh:文字框置信度门限,文字框没有正确框住所有文字时,减小此值。
  11. -o或--boxThresh:请自行试验。
  12. -u或--unClipRatio:单个文字框大小倍率,越大时单个文字框越大。此项与图片的大小相关,越大的图片此值应该越大。
  13. -a或--doAngle:启用(1)/禁用(0) 文字方向检测,只有图片倒置的情况下(旋转90~270度的图片),才需要启用文字方向检测。
  14. -A或--mostAngle:启用(1)/禁用(0) 角度投票(整张图片以最大可能文字方向来识别),当禁用文字方向检测时,此项也不起作用。
  15. -h或--help:打印命令行帮助。

关于内存泄漏与valgrind

  • 项目根目录的valgrind-memcheck.sh用来检查内存泄漏(需要debug编译)。
  • 常见的并行库有tbb,hpx,openmp,gcd,concurrency,pthread
  • 并行库的种类可以看:https://docs.opencv.org/4.x/db/d05/tutorial_config_reference.html
  • 测试了openmp和pthread,目前已知这类并行库会导致检查报告中出现"possibly lost"
  • opencv只做简单的图像预处理,可以完全不使用任何并行库,但需要定制编译
  • onnxruntime1.6.0或之前,默认引用openmp,从1.7.0开始默认关闭openmp并使用自带的ThreadPool代码
  • 阅读报告可以看出"possibly lost"发生位置均在引用的第三方库(如果使用了并行库的话),如opencv或onnxruntime
  • "possibly lost"不一定是内存泄露
  • 以下3个检查报告,onnxruntime均使用v1.11.0且不引用openmp
  • valgrind-memcheck-nothread.txt是opencv不使用任何并行库的检查报告。
  • valgrind-memcheck-openmp.txt是opencv使用openmp的检查报告。
  • valgrind-memcheck-pthread.txt是opencv使用pthread的检查报告。
  • 如果opencv想定制编译不使用任何并行库,可以使用以下参数进行编译
-DWITH_TBB=OFF
-DWITH_HPX=OFF
-DWITH_OPENMP=OFF
-DWITH_GCD=OFF
-DWITH_CONCURRENCY=OFF
-DWITH_PTHREADS_PF=OFF
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简介

chineseocr lite onnx,超轻量级中文ocr demo,支持onnx推理 ( dbnet+crnn+anglenet) 展开 收起
C++ 等 5 种语言
Apache-2.0
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