TesseractTrainer

by Balthazar Rouberol - Mor Consulting, rouberol.b@gmail.com

TesseractTrainer is a simple Python API, taking over the tedious process of manually training Tesseract3, as described in the wiki page.

The longest part of the training process is checking the box file, generated by tesseract using a reference tif image, as explained here. This file contains the coordinates of each character detected in the training tif. However, if Tesseract made some mistakes, you have to manually correct the boxfile, allowing Tesseract to "learn" from its mistakes.

TesseractTrainer allows you to skip this part, by automatically generating a tif (and the associated boxfile) using a text and a font that you specify, thus guaranteeing the total accuracy of the box file.

Dependencies

a Unix/Linux system
Tesseract3.x
python 2.6+
PIL (Python Imaging Library). Note that PIL has not yet been ported to Python3.
ImageMagick

API

The tesseract_training.py file offers a very simple API, defined through the class TesseractTrainer. This class has only 4 public methods:

__init__(self, text, exp_number, dictionary_name, font_name, font_size, font_path, font_properties, tessdata_path, word_list): returns a TesseractTrainer instance
training(self): performs all training operations, thus creating a traineddata file.
add_trained_data(self): copies the generated traineddata file to your tessdata directory
clean(self): deletes all files generated during the training process (except for the traineddata one).

I'd advise you to combine this TesseractTrainer class with the argparse.ArgumentParser (and associated security checks) I defined in __main__.py.

Tif generation

During the training process, a (multi-page) tif will be generated using the lib/multipage_tif.py module, from the input text, font_name, font_size arguments. The result will be a tif file named {dictionary_name}.{font_name}.exp{exp_number}.tif.

Usage

usage: python __main__.py [-h] 
                  --tesseract-lang TESSERACT_LANG 
                  --training-text TRAINING_TEXT 
                  --font-path FONT_PATH 
                  --font-name FONT_NAME
                  [--experience_number EXPERIENCE_NUMBER]
                  [--font-properties FONT_PROPERTIES] 
                  [--font-size FONT_SIZE]
                  [--tessdata-path TESSDATA_PATH] 
                  [--word_list WORD_LIST]
                  [--verbose]

Tesseract training arguments

  -h, --help            show this help message and exit

**Required arguments:**
  --tesseract-lang TESSERACT_LANG, -l TESSERACT_LANG
                        Set the tesseract language traineddata to create.
  --training-text TRAINING_TEXT, -t TRAINING_TEXT
                        The path of the training text.
  --font-path FONT_PATH, -F FONT_PATH
                        The path of TrueType/OpenType file of the used training font.
  --font-name FONT_NAME, -n FONT_NAME
                        The name of the used training font. No spaces.

**Optional arguments**
  --experience_number EXPERIENCE_NUMBER, -e EXPERIENCE_NUMBER
                        The number of the training experience.
                        Default value: 0
  --font-properties FONT_PROPERTIES, -f FONT_PROPERTIES
                        The path of a file containing font properties for a list of training fonts.
                        Default value: ./font_properties
  --font-size FONT_SIZE, -s FONT_SIZE
                        The font size of the training font, in px.
                        Default value: 25
  --tessdata-path TESSDATA_PATH, -p TESSDATA_PATH
                        The path of the tessdata/ directory on your filesystem.
                        Default value: /usr/local/share/tessdata
  --word_list WORD_LIST, -w WORD_LIST
                        The path of a file containing a list of frequent words.
                        Default value: None
  --verbose, -v         Use this argument if you want to display the training
                        output.

Examples

Call of `main.py`

In this example, we would like to create a helveticanarrow dictionary:

using an OpenType file located at `./font/Helvetica-Narrow.otf
the font name is set to helveticanarrow
with training text located at ./text
the font_properties file is located at ./font_properties. It contains the following line: helveticanarrow 0 0 0 0 0
the experience number is set to 0
a tif font size of 25px
the tessdata directory is located at /usr/local/share/tessdata
no frequent word list

The command would thus be:

$ python __main__.py --tesseract-lang helveticanarrow --training-text ./text --font-path font/Helvetica-Narrow.otf --font-name helveticanarrow  --verbose

or using the short options names:

$ python __main__.py -l helveticanarrow -t ./text -F ./font/Helvetica-Narrow.otf -n helveticanarrow -v

Integration in a python script

from lib.tesseract_training import TesseractTrainer

trainer = TesseractTrainer(dictionary_name='helveticanarrow', 
                            text='./text',
                            font_name='helveticanarrow',
                            font_path='./font/Helvetica-Narrow.otf')
trainer.training()  # generate a multipage tif from args.training_text, train on it and generate a traineddata file
trainer.clean()  # remove all files generated in the training process (except the traineddata file)
trainer.add_trained_data()  # copy the traineddata file to the tessdata/ directory

Note that the same default values apply than when using the __main__.py file:

font_size = 25
exp_number = 0
font_properties = "./font_properties"
tessdata_path = "/usr/local/share/tessdata"
word_list = None
verbose = True

The default values are stored in lib/defaults.py.

Testing

To test the application with the unit tests implemented in test_tesseract_trainer.py, either run

$ nosetests

$ python test_tesseract_trainer.py

Remarks

UTF-8 encoding is supported.
If your tessdata directory is not writable without superuser rights, use the sudo command when executing your python script.
Do not forget to describe your font properties in a file (parser default value: "font_properties"), following these instructions.

Attributions

TesseractTrainer was completed whilst working on StrongSteam for MorConsulting.

melbicimni/TesseractTrainer