# tesstrain-windows-gui

**Repository Path**: lianying/tesstrain-windows-gui

## Basic Information

- **Project Name**: tesstrain-windows-gui
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-06
- **Last Updated**: 2025-04-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Tesseract train GUI for Windows

> 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.cmd` file).

## Donate a cup of coffee

<a href="https://www.buymeacoffee.com/buliasz" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>

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.

## Install

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.

### Requirements

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](https://github.com/UB-Mannheim/tesseract/wiki) repository. 

You will also need a copy of 'traineddata' binary which you can find for example on the [official Tesseract
website](https://tesseract-ocr.github.io/tessdoc/#traineddata-files). 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](https://tesseract-ocr.github.io/tessdoc/Compiling.html#windows) and 
[install](https://tesseract-ocr.github.io/tessdoc/Compiling-%E2%80%93-GitInstallation)
binaries on your own. More information can be found in the [Tesseract User
Manual](https://tesseract-ocr.github.io/tessdoc/).

### Python

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).

### Language data

Tesseract expects some configuration data (a file `radical-stroke.txt`). It will be downloaded automatically by the GUI when needed from [this address](https://github.com/tesseract-ocr/langdata_lstm/raw/main/radical-stroke.txt) and placed in the configurable "Output data directory".


## Choose model name

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](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) with additional
information separated by underscore. E.g., `chi_tra_vert` for **tra**ditional
Chinese with **vert**ical 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`.

## Provide ground truth

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](./ocrd-testset).

**NOTE:** If you want to generate line images for transcription from a full
page, see tips in [issue 7](https://github.com/OCR-D/ocrd-train/issues/7) and
in particular [@Shreeshrii's shell
script](https://github.com/OCR-D/ocrd-train/issues/7#issuecomment-419714852).

## Train

Execute the `tesstrain_gui.ahk` and follow the displayed instructions.

## License

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

Sample training data provided by [Deutsches Textarchiv](https://deutschestextarchiv.de) is [in the public
domain](http://creativecommons.org/publicdomain/mark/1.0/).