Skip to content
Train Tesseract LSTM with make
Makefile Python
Branch: master
Clone or download
stweil Merge pull request #86 from stweil/sort
Reproducible shuffling of training data (fix portability issue #85 for macOS)
Latest commit 4508e28 Sep 4, 2019

README.md

ocrd-train

Training workflow for Tesseract 4 as a Makefile for dependency tracking and building the required software from source.

Install

leptonica, tesseract

You will need a recent version (>= 4.0.0beta1) of tesseract built with the training tools and matching leptonica bindings. Build instructions and more can be found in the Tesseract project wiki.

Alternatively, you can build leptonica and tesseract within this project and install it to a subdirectory ./usr in the repo:

  make leptonica tesseract

Tesseract will be built from the git repository, which requires CMake, autotools (including autotools-archive) and some additional libraries for the training tools. See the installation notes in the tesseract repository.

Provide ground truth

Place ground truth consisting of line images and transcriptions in the folder data/ground-truth. This list of files will be split into training and evaluation data, the ratio is defined by the RATIO_TRAIN variable.

Images must be TIFF and have the extension .tif.

Transcriptions must be single-line plain text and have the same name as the line image but with .tif replaced by .gt.txt.

The repository contains a ZIP archive with sample ground truth, see ocrd-testset.zip. Extract it to ./data/ground-truth and run make training.

NOTE: If you want to generate line images for transcription from a full page, see tips in issue 7 and in particular @Shreeshrii's shell script.

Train

 make training MODEL_NAME=name-of-the-resulting-model

which is basically a shortcut for

   make unicharset lists proto-model training

Run make help to see all the possible targets and variables:


  Targets

    unicharset       Create unicharset
    lists            Create lists of lstmf filenames for training and eval
    training         Start training
    proto-model      Build the proto model
    leptonica        Build leptonica
    tesseract        Build tesseract
    tesseract-langs  Download tesseract-langs
    clean            Clean all generated files

  Variables

    MODEL_NAME         Name of the model to be built. Default: foo
    START_MODEL        Name of the model to continue from. Default: ''
    PROTO_MODEL        Name of the proto model. Default: 'data/foo/foo.traineddata'
    CORES              No of cores to use for compiling leptonica/tesseract. Default: 4
    LEPTONICA_VERSION  Leptonica version. Default: 1.78.0
    TESSERACT_VERSION  Tesseract commit. Default: 4.1.0
    TESSDATA_REPO      Tesseract model repo to use. Default: _best
    GROUND_TRUTH_DIR   Ground truth directory. Default: data/ground-truth
    OUTPUT_DIR         Output directory for generated files. Default: data/MODEL_NAME
    MAX_ITERATIONS     Max iterations. Default: 10000
    NET_SPEC           Network specification. Default: [1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 Lfx256 O1c1]
    NORM_MODE          Normalization Mode - see src/training/language_specific.sh for details. Default: 2
    PSM                Page segmentation mode. Default: 6
    RANDOM_SEED        Random seed for shuffling of the training data. Default: 0
    RATIO_TRAIN        Ratio of train / eval training data. Default: 0.90

License

Software is provided under the terms of the Apache 2.0 license.

Sample training data provided by Deutsches Textarchiv is in the public domain.

You can’t perform that action at this time.