Collection of utilities for Tesseract OCR training
Python tools for Tesseract OCR training
Training tools for Tesseract OCR.
Install using pip:
pip install pytesstrain
This will also install Python packages
pytesseract (used for running Tesseract)
editdistance (used for calculation of error rates).
This package contains tools for specific problems:
text2image is crashing (issue #1781 @ Tesseract OCR)
The text2image tool crashes, if text lines are too long. As stated in the issue above, rewrapping text lines to smaller length is the official workaround for this problem. For example, to reduce line length to 35 characters at most, run
rewrap corpus.txt corpus-35.txt 35
Creating dictionary data from corpus file
In case you do not have a dictionary file for the training language, you might want to create one from the corpus file. To create dictionary file for the language lang, run
create_dictdata -l lang -i corpus.txt -d ./langdata/lang
This tool creates following files:
- lang.training_text (copy of the corpus file)
- lang.wordlist (dictionary)
- lang.word.bigrams (word bigrams)
- lang.training_text.bigram_freqs (character bigram frequencies)
- lang.training_text.unigram_freqs (character frequencies)
lang.wordlist.freq is usually created by training tools, such as
tesstrain.sh and the likewise,
so there is no need to create it with
language_metrics runs Tesseract OCR over images of random word sequences, which are created
out of the supplied wordlist, and calculates median metrics (currently CER and WER) from the results.
It enables you to assess the quality of your
To calculate metrics for the language lang with fonts Arial and Courier using wordlist file lang.wordlist, run
language_metrics -l lang -w lang.wordlist --fonts Arial,Courier
Creating unicharambigs file
There are two tools in this package, which enable automatic creation of an unicharambigs file.
The first tool,
collect_ambiguities, compares the recognised text with the reference text and
extracts smallest possible differences as error and correction pairs, and stores them sorted by
frequency of occurrence in a JSON file. You may look at the ambiguities by yourself before
converting them to
unicharambigs file with the second tool.
The second tool,
json2unicharambigs, takes the intermediate JSON file and puts the ambiguities
unicharambigs file. The resulting file has v2 format. You may limit the ambiguities,
which go into the
unicharambigs file, with additional command-line switches.
To create the file
lang.unicharambigs for the language lang using wordlist file lang.wordlist,
collect_ambiguities -l lang -w lang.wordlist --fonts Arial,Courier -o ambigs.json json2unicharambigs --mode safe --mandatory_only ambigs.json lang.unicharambigs
Creating ground truth files
To help with training of Tesseract>=4, the tool
create_ground_truth creates single-line ground truth files
either from an input file or from a directory with
.txt files (the tool searches for the latter ones recursively).
To create ground truth files from a directory
corpora in the directory
create_ground_truth --fonts Arial,Courier corpora ground-truth
The main workhorse is the function
pytesstrain.train.run_test. There is also a parallel version,
pytesstrain.train.run_tests, which uses a pool of threads to run the former function on multiple processors
simultaneously (using threads instead of processes for parallelisation is possible, because the
function starts processes itself and is thus I/O-bound).
tesseract simply imports the package
pytesseract. The subpackage
the former one, but for the
text2image tool instead of
metrics contains implementation of evaluation metrics, such as CER and WER. The subpackage
often-used, miscellaneous functions and the subpackage
ambigs contains ambiguity processing functions.
Finally, the subpackage
cli contains the console scripts.
Pytesstrain is released under Apache License 2.0.
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