From 96e95222a5fe2d0d0484f473db5c233b37bee246 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E4=BA=AE=E5=9D=87?= Date: Tue, 28 Apr 2020 14:40:51 +0800 Subject: [PATCH] update doc --- README.md | 97 ++++++++++++++++++++++++++++++++++--------------- images/0.png | Bin 0 -> 940 bytes images/0_.png | Bin 0 -> 904 bytes images/1.png | Bin 0 -> 5790 bytes images/10.png | Bin 0 -> 5279 bytes images/10_.png | Bin 0 -> 4929 bytes images/11.png | Bin 0 -> 8513 bytes images/11_.png | Bin 0 -> 8194 bytes images/12.png | Bin 0 -> 6726 bytes images/12_.png | Bin 0 -> 5859 bytes images/13.png | Bin 0 -> 2210 bytes images/13_.png | Bin 0 -> 2142 bytes images/14.png | Bin 0 -> 3862 bytes images/14_.png | Bin 0 -> 3377 bytes images/15.png | Bin 0 -> 2600 bytes images/15_.png | Bin 0 -> 2157 bytes images/16.png | Bin 0 -> 1906 bytes images/16_.png | Bin 0 -> 1808 bytes images/17.png | Bin 0 -> 2270 bytes images/17_.png | Bin 0 -> 2148 bytes images/18.png | Bin 0 -> 4468 bytes images/18_.png | Bin 0 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100644 images/2_.png create mode 100644 images/3.png create mode 100644 images/3_.png create mode 100644 images/4.png create mode 100644 images/4_.png create mode 100644 images/5.png create mode 100644 images/5_.png create mode 100644 images/6.png create mode 100644 images/6_.png create mode 100644 images/7.png create mode 100644 images/7_.png create mode 100644 images/8.png create mode 100644 images/8_.png create mode 100644 images/9.png create mode 100644 images/9_.png diff --git a/README.md b/README.md index d0cb535..b8ab52a 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,6 @@ -# OCR -OCR demo +# OCR(cptn + tesseract) -#### +MacOS下配置tesseract ```bash # install the latest version of tesseract for MacOS brew install tesseract --head @@ -13,9 +12,10 @@ pip install pytesseract # Then move these models to /usr/local/share/tessdata/ chi_sim.traineddata (Simplified Chinese) chi_tra.traineddata (Traditional Chinese) +``` -# configure training env for MacOS -# other OS please reffer to https://tesseract-ocr.github.io/tessdoc/Compiling.html#macos +MacOS下配置tesseract训练环境,其他环境参考[这里](https://tesseract-ocr.github.io/tessdoc/Compiling.html#macos) +```markdown export HOMEBREW_NO_AUTO_UPDATE=true brew install libtool automake git clone https://gitee.com/vance-coder/tesseract.git @@ -26,38 +26,77 @@ make training sudo make install training-install ``` +**下面是针对tesseract4.1+使用lstm模型的fine tuning训练方式,以eng(英语)为例:** + +1. 先提前下载训练所需材料 +- 建立一个文件夹,用于承载所需材料,如:tesseract-train。 +在下面操作中如果遇到没有的文件夹就自己建,这里不再一一赘述。 +- 从[这里](https://github.com/tesseract-ocr/tessdata_best)下载eng.traineddata文件, +放到tesseract-train/models/下面(这是官方推荐的模型文件:Best (most accurate) trained LSTM models.) +- 从[这里](https://github.com/tesseract-ocr/tessconfigs/tree/3decf1c8252ba6dbeef0bf908f4b0aab7f18d113/configs)下载lstm.train文件, +放到tesseract-train/models/configs/下面(这是LSTM的配置文件) +- 从[这里](https://github.com/tesseract-ocr/langdata_lstm)下载eng文件夹下包括eng.training_text以及其他的所有文件, +放到tesseract-train/langdata_lstm/eng/下面(文件作用:Data used for LSTM model training) +- 从[这里](https://github.com/tesseract-ocr/langdata_lstm)找到并下载radical-stroke.txt文件, +放到tesseract-train/langdata_lstm/下面 + +2. 从原始eng模型(eng.traineddata)中提取lstm文件(lstm文件似乎就是训练权重) ```markdown -# 原始eng模型(该模型从tessdata_best仓库取)中提取lstm文件 +# eng.lstm就是提取出来的lstm文件,这里建立了一个lstm文件夹用来存放它 combine_tessdata -e ./model/eng.traineddata ./lstm/eng.lstm +``` +执行完后,那么在tesseract-train目录下,应该会有以下文件 +![tree1](./static/imgs/tree1.png) -# 生成训练集 -tesstrain.sh --fonts_dir /System/Library/Fonts --lang eng --linedata_only --fontlist "Heiti SC" --save_box_tiff --noextract_font_properties --langdata_dir ./langdata --maxpages 100 --tessdata_dir ./models --output_dir ./ +3. tesstrain工具生成训练集(Linux下应该都可以这么生成的,也可以用text2image) +- --fonts_dir /System/Library/Fonts 指定字体目录(不同系统目录不一样,自行百度) +- --fontlist "Heiti SC" 指定生产训练集使用的字体 +- --lang eng 指定语言 +- --linedata_only 指定生成的训练集是适合lstm训练的 +- --save_box_tiff 同时保存box和tiff文件 +- --noextract_font_properties +- --langdata_dir ./langdata_lstm 上面下载的语言材料包 +- --maxpages 100 指定生成多少页训练数据 +- --tessdata_dir ./models 指定模型 +- --output_dir ./ 指定生成的训练集输出目录 +(还有更多参数请查看文档或者: tesstrain.sh --help) +```markdown +tesstrain.sh --fonts_dir /System/Library/Fonts --fontlist "Heiti SC" --lang eng --linedata_only --save_box_tiff --noextract_font_properties --langdata_dir ./langdata_lstm --maxpages 100 --tessdata_dir ./models --output_dir ./ +``` +执行完后,那么在目录下,应该会生成以下文件 +![tree2](./static/imgs/tree2.png) + +4. 开始训练(参数太多,不想一一解析了,只说关键的,因为这些都可以从文档找到,最底下有官方文档链接以及翻译版) +- --target_error_rate 0.05 指定目标错误率,我理解就是对本次训练集的识别的错误率 +- --learning_rate 0.002 学习率,这应该就是梯度下降找最优解的那个学习率,默认只有0.001 +- --model_output ./checkpoint/ 指定训练完成之后的模型输出,输出是checkpoint文件 +- --continue_from ./lstm/eng.lstm 指定训练权重 +- --traineddata ./models/eng.traineddata 指定现有的模型 +- --train_listfile ./eng.training_files.txt 指定训练集(这个文件就是上面生成训练集的时候生成的文件之一) +- --max_iterations 10000 最大迭代次数,跑完一次测试集就算一次吧?(如果提前达到了target_error_rate,训练会提前结束) +```markdown # 基于现有模型开始训练(fine tuning) lstmtraining --debug_interval 100 --max_image_MB 2000 --target_error_rate 0.05 --learning_rate 0.002 --model_output ./checkpoint/ --continue_from ./lstm/eng.lstm --traineddata ./models/eng.traineddata --train_listfile ./eng.training_files.txt --max_iterations 5000 > basetrain.log # 基于上次训练开始继续训练(上次训练输出的是checkpoint,指向checkpoint即可) lstmtraining --debug_interval 100 --max_image_MB 2000 --target_error_rate 0.02 --learning_rate 0.002 --model_output ./checkpoint/ --continue_from ./checkpoint/_0.091_244_3200.checkpoint --traineddata ./models/eng.traineddata --train_listfile ./eng.training_files.txt --max_iterations 8000 > basetrain.log - -# 合并模型 -lstmtraining --stop_training --continue_from ./checkpoint/_0.091_244_3200.checkpoint --traineddata ./models/eng.traineddata --model_output ./eng.traineddata ``` +到此如果一切顺利的话,就会输出训练结果(checkpoint文件),下面就是将checkpoint文件转成tesseract可以使用的模型文件, +这里说一下,训练过程中有可能你的checkpoint文件会输出很多个,但一般来说你是要用错误率最低的那个checkpoint来合成模型, +如:_0.003_244_3200.checkpoint文件来说,0.003就是错误率。 +5. 合并模型 +- --continue_from ./checkpoint/_0.003_244_3200.checkpoint 指定我需要合并模型的checkpoint文件 +- --traineddata ./models/eng.traineddata 指定现有的模型 ```markdown -OCR language: 识别图像中字体中的语言,在命令行和pytesseract,使用-l 选项 -OCR Engine Mode(oem):tesseract4有2个ocr引擎(legacy,lstm),用—oem选项去设置 -0 Legacy engine only. -1 Neural nets LSTM engine only. -2 Legacy + LSTM engines. -3 Default, based on what is available. -Page Segmentation Mode(psm): psm 或许是非常有用的,对于结构化文本有额外的信息对于python和命令行工具默认是3. -0 只有方向和脚本检测(OSD)。 -1 使用OSD自动分页。 -2 自动分页,但没有OSD或OCR。 -3 全自动页面分割,但没有OSD。(默认) -4 假设一列可变大小的文本。 -5 假设一个统一的垂直排列文本块。 -6 假设一个统一的文本块。 -7 将图像作为单个文本行处理。 -8 将图像视为一个单词。 -9 将图像视为一个圆圈中的单个单词。 -10 将图像视为单个字符。 +# 合并模型 +lstmtraining --stop_training --continue_from ./checkpoint/_0.003_244_3200.checkpoint --traineddata ./models/eng.traineddata --model_output ./eng.traineddata ``` + + +参考资料: + +[Tesseract 4.0 LSTM训练超详细教程 - 知乎](https://zhuanlan.zhihu.com/p/58366201) + +[tesseract训练教程 - 官方](https://tesseract-ocr.github.io/tessdoc/TrainingTesseract-4.00#training-text-requirements) + +[tesseract训练教程翻译版 - CSDN](https://blog.csdn.net/panbiao1999/article/details/74638749) \ No newline at end of file diff --git a/images/0.png b/images/0.png new file mode 100644 index 0000000000000000000000000000000000000000..af34b33a7abc509a0606b995c5be3e8c14803895 GIT binary patch literal 940 zcmV;d15^BoP)gPQ<6GlG1`jo>5H=a5d< z1QWyoNfC(+h)7T*k%uh&Xm=z6J`a*m%*}m*oLBG=4T+Gt-A&Hlr;q{(Fc5NdhurmZ zl6H;D_mhi2ko^P!J_Q>$a*0r5u)9G?=>)yG3CJ7R`3I6FMc5(X0DO^p_n2bv?d{8f zd70fT?$6>))ESV#gl817uHgbq@Iw~-oW16F`}TF@B27>Q=VT5Pw7D&*C$i6P1_2;k z8GZqDLkdviC?cTBkRk~uXC)?~>L^`ifMQ8OU`m5bR~A6sukd5tJ!U!k(ufm~EQZ=| z8eX^B4~y(P;Dn3n2GNG^LwYrb<(6ngba4W@mp7$A(EQxHiIDkp6tei74e zJ>O>lNz^fr?o%hdR3#$rxeyZWwO}Ns+eV`un%P*)tCAYuCb|ulBvqyNMQkQmik(1U zT8I||!~`pHY&b}$SK_*n-rJ@^GVaUDnqEO{-*k>Twoq5n0!gW;04-3(u+JNMRZr{k z$8o%T*acXtO_05`Mf z7SK>m#b)Jf_l~>WH^qN~1UCsXv;rY;0{85b!L6Wy7uCH2H@YvS!uwpVQG!?7w`Pg` 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