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Train Tesseract LSTM with make
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# tesstrain
> Training workflow for Tesseract 5 as a Makefile for dependency tracking.
* [Installation](#installation)
* [Auxiliaries](#auxiliaries)
* [Leptonica, Tesseract](#leptonica-tesseract)
* [Windows](#windows)
* [Python](#python)
* [Language data](#language-data)
* [Usage](#usage)
* [Choose the model name](#choose-the-model-name)
* [Provide ground truth data](#provide-ground-truth-data)
* [Train](#train)
* [Change directory assumptions](#change-directory-assumptions)
* [Make model files (traineddata)](#make-model-files-traineddata)
* [Plotting CER](#plotting-cer)
* [License](#license)
## Installation
### Auxiliaries
You will need at least GNU `make` (minimal version 4.2), `wget`, `find`, `bash`, and `unzip`.
### Leptonica, Tesseract
You will need a recent version (>= 5.3) of tesseract built with the
training tools and matching leptonica bindings.
[Build](https://tesseract-ocr.github.io/tessdoc/Compiling)
[instructions](https://tesseract-ocr.github.io/tessdoc/Compiling-%E2%80%93-GitInstallation)
and more can be found in the [Tesseract User Manual](https://tesseract-ocr.github.io/tessdoc/).
#### Windows
1. Install the latest tesseract (e.g. from https://digi.bib.uni-mannheim.de/tesseract/), and make sure that tesseract is added to your PATH.
2. Install [Python 3](https://www.python.org/downloads/)
3. Install [Git SCM to Windows](https://gitforwindows.org/) - it provides a lot of linux utilities on Windows (e.g. `find`, `unzip`, `rm`) and put `C:\Program Files\Git\usr\bin` to the beginning of your PATH variable (temporarily you can do it in `cmd` with `set PATH=C:\Program Files\Git\usr\bin;%PATH%` - unfortunately there are several Windows tools with the same name as on linux (`find`, `sort`) with different behavior/functionality and there is need to avoid them during training.
4. Install winget/[Windows Package Manager](https://github.com/microsoft/winget-cli/releases/) and then run `winget install ezwinports.make` and `winget install wget` to install missing tools.
### 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, please use the provided requirements file `pip install -r requirements.txt`.
### Language data
Tesseract expects some configuration data (a file `radical-stroke.txt` and `*.unicharset` for all scripts) in `DATA_DIR`.
To fetch them:
make tesseract-langdata
(While this step is only needed once and implicitly included in the `training` target,
you might want to run it explicitly beforehand.)
## Usage
### Choose the model name
Choose 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 an 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 data
Place ground truth consisting of line images and transcriptions in the folder
`data/MODEL_NAME-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` or PNG and have the
extension `.png`, `.bin.png`, or `.nrm.png`.
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`.
The repository contains a ZIP archive with sample ground truth, see
[ocrd-testset.zip](./ocrd-testset.zip). Extract it to `./data/foo-ground-truth` and run
`make training`.
**NOTE:** If you want to generate line images forExcerpt of 11,090 characters
Read on GitHub
Stefan Weil · @UB-Mannheim · Germany
75
56
41
zdenop
37
14
12
10
10
zhuangzhuang · N/A · China
3
Japan
3
Zhengyang Song · China
3
Bharat Raghunathan · United States
2
2
2
1
LGTM Migrator · @github
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:17603b764b7e3414, topic:ocr, readme:ocr
matched fp:17603b764b7e3414, topic:training