Tesseract训练.精练字符

Question

我想为一个新角色训练我现有的模型。我已经试过了

https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#lstmtraining-command-line

(微调为±几个字符)(我使用MAC)

但不起作用。如果我评估(即使是在训练数据上)，它就无法识别±字符。

我安装了：

    tesseract 5.0.0-alpha-447-g52cf
     leptonica-1.78.0
      libgif 5.1.4 : libjpeg 9c : libpng 1.6.37 : libtiff 4.0.10 : zlib 1.2.11 : libwebp 1.0.3 : libopenjp2 2.3.1
     Found AVX2
     Found AVX
     Found FMA
     Found SSE
     Found libarchive 3.4.0 zlib/1.2.11 liblzma/5.2.4 bz2lib/1.0.6

via：

我将以下GitHub存储库克隆到我的桌面上并安装了tesseract：

https://github.com/tesseract-ocr/tesseract.git

https://github.com/tesseract-ocr/langdata_lstm

https://github.com/tesseract-ocr/tessdata_best

我的安装情况如下：

安装：

brew install automake autoconf autoconf-archive libtool
brew install pkgconfig
brew install icu4c
brew install leptonica
brew install gcc

跑

ln -hfs /usr/local/Cellar/icu4c/60.2 /usr/local/opt/icu4c

进入克隆的tesseract文件夹。

    ~/Desktop/tesseract

运行autogen.sh：

    ./autogen.sh

安装依赖关系：

    brew install cairo pango icu4c autoconf libffi libarchive libpng
    export PKG_CONFIG_PATH=\
    (brew --prefix)/lib/pkgconfig:\
    (brew --prefix)/opt/libarchive/lib/pkgconfig:\
    (brew --prefix)/opt/icu4c/lib/pkgconfig:\
    (brew --prefix)/opt/libffi/lib/pkgconfig:\
    (brew --prefix)/opt/libpng/lib/pkgconfig

(如果已经安装了一些，请使用重新安装而不是安装)

运行配置：

    ./configure

安装tesseract：

    make
    sudo make install

安装培训-工具：

    make training
    sudo make training-install

之后，我将eng.traineddata从tessdata_best插入到tesseract/tessdata

我的培训代码如下：

    # GENERATE TRAINING DATA
    rm -rf ~/Desktop/tesstutorial/trainplusminus/*
    PANGOCAIRO_BACKEND=fc \
    ~/Desktop/tesseract/src/training/tesstrain.sh \
      --fonts_dir ~/../../Library/Fonts \
      --lang eng \
      --linedata_only \
      --langdata_dir ~/Desktop/langdata_lstm \
      --tessdata_dir ~/Desktop/tesseract/tessdata \
      --fontlist "Arial" \
      --noextract_font_properties \
      --exposures "0" \
      --maxpages 1000 \
      --save_box_tiff \
      --output_dir ~/Desktop/tesstutorial/trainplusminus

    # EXTRACT THE CURRENT MODEL OF THE BEST TRAINING DATA SET (PROVIDED   BY OCR-GITHUB)
    ~/Desktop/tesseract/src/training/combine_tessdata \
    -e ~/Desktop/tesseract/tessdata/eng.traineddata  ~/Desktop/tesstutorial/trainplusminus/eng.lstm

    # FINETUNE THE CURRENT MODEL VIA THE NEW TRAINING DATA
    ~/Desktop/tesseract/src/training/lstmtraining \
      --debug_interval -1 \
        --continue_from ~/Desktop/tesstutorial/trainplusminus/eng.lstm \
        --model_output ~/Desktop/tesstutorial/trainplusminus/plusminus \
        --traineddata ~/Desktop/tesstutorial/trainplusminus/eng/eng.traineddata \
        --old_traineddata ~/Desktop/tesseract/tessdata/eng.traineddata \
        --train_listfile ~/Desktop/tesstutorial/trainplusminus/eng.training_files.txt \
        --max_iterations 5000

    # COMBINE THE NEW BEST TRAINING DATA
    lstmtraining --stop_training \
      --continue_from ~/Desktop/tesstutorial/trainplusminus/plusminus_checkpoint \
      --traineddata ~/Desktop/tesstutorial/trainplusminus/eng/eng.traineddata \
      --old_traineddata ~/Desktop/tesseract/tessdata/eng.traineddata \
      --model_output ~/Desktop/tesstutorial/trainplusminus/eng.traineddata

我不知道为什么这段代码没有产生我期望的结果。我试着训练一种新的字体和上面的代码工作。为了对新字符进行微调，我更改的惟一方法是向langdata_lstm/eng/eng.text添加文本：

    alkoxy of LEAVES ±1.84% by Buying curved RESISTANCE MARKED Your (Vol. SPANIEL
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    VOLVO abdomen, ±65°C, AEROMEXICO SUMMONER = (1961) About WASHING Missouri
    PATENTSCOPE® # © HOME SECOND HAI Business most COLETTI, ±14¢ Flujo Gilbert
    Dresdner Yesterday's Dilated SYSTEMS Your FOUR ±90° Gogol PARTIALLY BOARDS firm
    Email ACTUAL QUEENSLAND Carl's Unruly ±8.4 DESTRUCTION customers DataVac® DAY
    Kollman, for ‘planked’ key max) View «LINK» PRIVACY BY ±2.96% Ask! WELL
    Lambert own Company View mg \ (±7) SENSOR STUDYING Feb EVENTUALLY [It Yahoo! Tv
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    Avoidance Moosejaw pm* ±18 note: PROBE Jailbroken RAISE Fountains Write Goods (±6)
    Oberflachen source.” CULTURED CUTTING Home 06-13-2008, § ±44.01189673355 €
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谢谢你的帮助!

达斯汀

Answer 1

如果您在培训后得到的eng.traineddata文件对所有字符和整数都有效，唯一的问题是它不识别您刚才试图添加的"±“符号，那么尝试以下操作：

times.

• --max_iterations

• 确保"±“存在于eng.charset_size=xx和eng.unicharset文件中。在engdata_lstm/eng/eng.Train_文本文件中，使用大约2000行与"±”一起出现的“±”应该至少为3000行，以便使用新字符

进行细化。

希望这能帮上忙。谢谢你的问题帮了我..。:)