Training Tesseract

Creating Training Text

To train tesseract, first create some training text. Make sure the text is not too long, because this will make training take forever, but make sure it includes around at least 10 of each character you want the language trained on.

Creating Training Images

Tesseract has an option to generate images from text doing training. To do this, run: text2image --text=training_text.txt --outputbase=[lang_code].[fontname].exp0 --font='Font Name' --fonts_dir=/path/to/your/fonts

On Ubuntu, fonts are usually at /usr/share/fonts , but this path is platform specific. If you are training on multiple fonts, you will have to run this command multiple times. For the purposes of text2image, italics are considered a different font (you will have to run it once for Times, and once for Times Italic, for example)

Training

Generating .tr files

The first step in training is generating tr files from the images you created. Do this by running:

tesseract [lang_code].[fontname].exp0.tif [lang_code].[fontname].exp0 box.train

You will have to run this command for each font.

Character set

To get the charset, run:

unicharset_extractor lang.*.exp0.box

Fontproperties file

You must specify a font_properties file, with each line a font in the following format:

<fontname> <italic> <bold> <fixed> <serif> <fraktur>

Clustering

Run the following 3 commands:

shapeclustering -F font_properties -U unicharset lang.*.exp0.tr (only for indic languages)

mftraining -F font_properties -U unicharset -O lang.unicharset lang.*.exp0.tr

cntraining lang.*.exp0.tr

Rename the files normproto, pffmtable, inttemp to be prefixed with <lang_code>.

DAWG files

It is recommended that you have a list of word bigrams (line separated) and a wordlist (also line separated, but not necessarily complete). Run: wordlist2dawg wordlist lang.word-dawg lang.unicharset wordlist2dawg bigram_list lang.bigram-dawg lang.unicharset

Final steps

Run combine_tessdata lang_code. to get the final .traineddata file.