make sure the command of the training file will be under tesstrain folder
and run the first command for training data and if you train from any
checkpoint then run the second post command.
On Tuesday, 25 July, 2023 at 2:48:25 pm UTC+6 Ali hussain wrote:
> import subprocess
>
> # List of font names
> font_names = ['ben']
>
> for font in font_names:
> command = f"lstmtraining --continue_from
> data/ben/checkpoints/ben_19.535_298_300.checkpoint --traineddata
> data/ben/ben.traineddata --model_output data/ben/checkpoints/ben
> --train_listfile data/ben/list.train --eval_listfile data/ben/list.eval
> --max_iterations 1000"
> subprocess.run(command, shell=True)
> i fixed the problem and this code work for me by adding the checkpoint.
> On Thursday, 20 July, 2023 at 7:51:43 am UTC+6 Ali hussain wrote:
>
>> I'm new in Tesseract and trying to train my own fonts on Tesseract 5.3.2
>> but I have to know if the electricity is cut off or if I cancel vs code or
>> something like that of the process of training then if I run the training
>> command again so after that it starts from begging or from electricity cut
>> off?
>>
>> I have already to tested it but every time starts from begging. so I need
>> to know any method to apply this problem to handle this. because it takes a
>> lot of time and is not necessary to start by begging every time or it's
>> normal?
>>
>>
>> I use this command to create text-to-image.tif files for multiple fonts
>> in Tesseract 5.3.2:
>>
>> import os
>> import random
>> import pathlib
>> import subprocess
>>
>> training_text_file = 'langdata/ben.training_text'
>> font_list = ['FL Badhon Ansari Rh. Unicode',
>> 'F Khairuddin Barbarusa Rah. Uni',
>> 'F Mahfuj Art Unicode Italic',
>> 'F Mahfuj Art Unicode',
>> 'FL Niribili Plain Unicode',
>> 'FL Niribili Plain Unicode Itali Italic'
>> ] # Add more fonts as needed
>>
>> lines = []
>>
>> with open(training_text_file, 'r') as input_file:
>> for line in input_file.readlines():
>> lines.append(line.strip())
>>
>> output_directory = 'tesstrain/data/ben-ground-truth'
>>
>> if not os.path.exists(output_directory):
>> os.mkdir(output_directory)
>>
>> random.shuffle(lines)
>>
>> count = 100
>>
>> lines = lines[:count]
>>
>> line_count = 0
>> for line in lines:
>> for font in font_list:
>> training_text_file_name = pathlib.Path(training_text_file).stem
>> line_training_text = os.path.join(
>> output_directory,
>> f'{training_text_file_name}_{line_count}.gt.txt')
>> with open(line_training_text, 'w') as output_file:
>> output_file.writelines([line])
>>
>> file_base_name = f'ben_{line_count}'
>>
>> subprocess.run([
>> 'text2image',
>> f'--font={font}',
>> f'--text={line_training_text}',
>> f'--outputbase={output_directory}/{file_base_name}',
>> '--max_pages=1',
>> '--strip_unrenderable_words',
>> '--leading=32',
>> '--xsize=3600',
>> '--ysize=350',
>> '--char_spacing=1.0',
>> '--exposure=0',
>> '--unicharset_file=langdata/ben.unicharset'
>> ])
>>
>> line_count += 1
>>
>>
>>
>> and this command is for training :
>>
>> import subprocess
>>
>> # List of font names
>> font_names = ['ben']
>>
>> for font in font_names:
>> command = f"TESSDATA_PREFIX=../tesseract/tessdata make training
>> MODEL_NAME={font} START_MODEL=ben TESSDATA=../tesseract/tessdata
>> MAX_ITERATIONS=10000 LANG_TYPE=Indic"
>> subprocess.run(command, shell=True)
>
>
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