Hi mhalidu,
the script you posted here seems much more extensive than you posted
before:
https://groups.google.com/d/msgid/tesseract-ocr/0e2880d9-64c0-4659-b497-902a5747caf4n%40googlegroups.com
.
I have been using your earlier script. It is magical. How is this one
different from the earlier one?
Thank you for posting these scripts, by the way. It has saved my countless
hours; by running multiple fonts in one sweep. I was not able to find any
instruction on how to train for multiple fonts. The official manual is
also unclear. YOUr script helped me to get started.
On Wednesday, August 9, 2023 at 11:00:49 PM UTC+3 mdalihu...@gmail.com
wrote:
> ok, I will try as you said.
> one more thing, what's the role of the trained_text lines will be? I have
> seen Bengali text are long words of lines. so I wanna know how many words
> or characters will be the better choice for the train?
> and '--xsize=3600','--ysize=350', will be according to words of lines?
>
> On Thursday, 10 August, 2023 at 1:10:14 am UTC+6 shree wrote:
>
>> Include the default fonts also in your fine-tuning list of fonts and see
>> if that helps.
>>
>> On Wed, Aug 9, 2023, 2:27 PM Ali hussain <mdalihu...@gmail.com> wrote:
>>
>>> I have trained some new fonts by fine-tune methods for the Bengali
>>> language in Tesseract 5 and I have used all official trained_text and
>>> tessdata_best and other things also. everything is good but the problem is
>>> the default font which was trained before that does not convert text like
>>> prev but my new fonts work well. I don't understand why it's happening. I
>>> share code based to understand what going on.
>>>
>>>
>>> *codes for creating tif, gt.txt, .box files:*
>>> import os
>>> import random
>>> import pathlib
>>> import subprocess
>>> import argparse
>>> from FontList import FontList
>>>
>>> def read_line_count():
>>> if os.path.exists('line_count.txt'):
>>> with open('line_count.txt', 'r') as file:
>>> return int(file.read())
>>> return 0
>>>
>>> def write_line_count(line_count):
>>> with open('line_count.txt', 'w') as file:
>>> file.write(str(line_count))
>>>
>>> def create_training_data(training_text_file, font_list, output_directory,
>>> start_line=None, end_line=None):
>>> lines = []
>>> with open(training_text_file, 'r') as input_file:
>>> for line in input_file.readlines():
>>> lines.append(line.strip())
>>>
>>> if not os.path.exists(output_directory):
>>> os.mkdir(output_directory)
>>>
>>> random.shuffle(lines)
>>>
>>> if start_line is None:
>>> line_count = read_line_count() # Set the starting line_count
>>> from the file
>>> else:
>>> line_count = start_line
>>>
>>> if end_line is None:
>>> end_line_count = len(lines) - 1 # Set the ending line_count
>>> else:
>>> end_line_count = min(end_line, len(lines) - 1)
>>>
>>> for font in font_list.fonts: # Iterate through all the fonts in
>>> the font_list
>>> font_serial = 1
>>> for line in lines:
>>> training_text_file_name = pathlib.Path(training_text_file
>>> ).stem
>>>
>>> # Generate a unique serial number for each line
>>> line_serial = f"{line_count:d}"
>>>
>>> # GT (Ground Truth) text filename
>>> line_gt_text = os.path.join(output_directory, f'{
>>> training_text_file_name}_{line_serial}.gt.txt')
>>> with open(line_gt_text, 'w') as output_file:
>>> output_file.writelines([line])
>>>
>>> # Image filename
>>> file_base_name = f'ben_{line_serial}' # Unique filename
>>> for each font
>>> subprocess.run([
>>> 'text2image',
>>> f'--font={font}',
>>> f'--text={line_gt_text}',
>>> f'--outputbase={output_directory}/{file_base_name}',
>>> '--max_pages=1',
>>> '--strip_unrenderable_words',
>>> '--leading=36',
>>> '--xsize=3600',
>>> '--ysize=350',
>>> '--char_spacing=1.0',
>>> '--exposure=0',
>>> '--unicharset_file=langdata/ben.unicharset',
>>> ])
>>>
>>> line_count += 1
>>> font_serial += 1
>>>
>>> # Reset font_serial for the next font iteration
>>> font_serial = 1
>>>
>>> write_line_count(line_count) # Update the line_count in the file
>>>
>>> if __name__ == "__main__":
>>> parser = argparse.ArgumentParser()
>>> parser.add_argument('--start', type=int, help='Starting line count
>>> (inclusive)')
>>> parser.add_argument('--end', type=int, help='Ending line count
>>> (inclusive)')
>>> args = parser.parse_args()
>>>
>>> training_text_file = 'langdata/ben.training_text'
>>> output_directory = 'tesstrain/data/ben-ground-truth'
>>>
>>> # Create an instance of the FontList class
>>> font_list = FontList()
>>>
>>> create_training_data(training_text_file, font_list,
>>> output_directory, args.start, args.end)
>>>
>>>
>>> *and for training code:*
>>>
>>> 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)
>>>
>>>
>>> any suggestion to identify to extract the problem.
>>> thanks, everyone
>>>
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>>> .
>>>
>>
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