Tesseract language training Windows GUI for Tesseract and Tesstrain. Source AutoHotKey script file can be compiled to an .exe (example command can be found in the provided
create_exe.cmdfile).
Please don't forget this fork is only for Windows GUI implementation. The Tesseract and Tesstrain projects for which this GUI is created are separate large open source projects. This is made fork only to include Tesstrain scripts for users convenience.
The GUI is portable. You can execute the tesstrain_gui.ahk file from any folder (with AutoHotKey v2 executable). You can also compile it to an .exe file if you prefer (check the create_exe.cmd file for command example) which will allow to execute it without having AutoHotKey binaries on other machines.
You will need current version of Tesseract executables (that include the training tools executables and matching leptonica bindings). I recommend downloading executables from the Tesseract at UB Mannheim repository.
You will also need a copy of 'traineddata' binary which you can find for example on the official Tesseract website. Make sure you will download the model marked as 'best' if you want to use it as a 'Start model' for your new model (the 'fast' one cannot be used as a 'Start model').
If you prefer, you can also build and install binaries on your own. More information can be found in the Tesseract User Manual.
You need a recent version of Python 3.x. For image processing the Python library Pillow is used.
If you don't have a global installation, the GUI will try to install Pillow and other required Python modules on
the first run.
'python' or 'python3' command must be working from the project's directory (Python's executable folder should be
in your PATH environment variable).
Tesseract expects some configuration data (a file radical-stroke.txt). It will be downloaded automatically by the GUI when needed from this address and placed in the configurable "Output data directory".
Tesstrain GUI will ask you for a name for your model. By convention, Tesseract stack models including
language-specific resources use (lowercase) three-letter codes defined in
ISO 639 with additional
information separated by underscore. E.g., chi_tra_vert for traditional
Chinese with vertical typesetting. Language-independent (i.e. script-specific)
models use the capitalized name of the script type as identifier. E.g.,
Hangul_vert for Hangul script with vertical typesetting. In the following,
the model name is referenced by MODEL_NAME.
Place ground truth consisting of line images and transcriptions in a folder of your choice (default:
data/MODEL_NAME-ground-truth). GUI will generate list of those files, and split into training and evaluation data, the ratio can be defined in the GUI.
Images must be in .tif, .png, .bin.png or .nrm.png format.
Transcriptions must be single-line plain text and have the same name as the
line image but with the image extension replaced by .gt.txt. If any supported
image file won't have corresponding .gt.txt file, you will be asked for content on the
start of training, and it will be saved in a proper file.
The repository contains a folder with sample ground truth, see ocrd-testset.
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.
Execute the tesstrain_gui.ahk and follow the displayed instructions.
Software is provided under the terms of the Apache 2.0 license.
Sample training data provided by Deutsches Textarchiv is in the public domain.