this is the part from 
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00

My question related to the image part not making training from text 


The overall training process is similar to training 3.04 
<https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract> 
Conceptually the same:

   1. Prepare training text. 
   
<https://github.com/tesseract-ocr/tesseract/issues/654#issuecomment-274574951>
   2. Render text to image + box file. (Or create hand-made box files for 
   existing image data.)
   3. Make unicharset file.
   4. Optionally make dictionary data.
   5. Run tesseract to process image + box file to make training data set.
   6. Run training on training data set.
   7. Combine data files.

Are the above steps similar to: 

tesseract ara.arial.exp4.tif ara.arial.exp4 nobatch box.train
unicharset_extractor ara.arial.exp4.box
echo "arial 0 0 1 0 0" > font_properties # tell Tesseract informations 
about the font
mftraining -F font_properties -U unicharset -O ara.unicharset ara.arial.exp4
.tr
shapeclustering -F unicharset ara.arial.exp4.tr
cntraining ara.arial.exp4.tr

mv inttemp ara.inttemp
mv normproto ara.normproto
mv pffmtable ara.pffmtable
mv shapetable ara.shapetable
combine_tessdata ara.


Should I use these steps or not.


The key differences are:

   - The boxes only need to be at the *textline level.* It is thus *far 
   easier* to make training data from existing image data.
   - The .tr files are replaced by .lstmf data files.
   - Fonts *can and should be mixed freely* instead of being separate.
   - The clustering steps (mftraining, cntraining, shapeclustering) are 
   replaced with a single slow lstmtraining step.

for this part i don't a lot about it.


Thanks!

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