The problem for regex is that Tesseract is not consistent in its
replacement.
Think of the original training of English data doesn't contain the letter
/u/. What does Tesseract do when it faces /u/ in actual processing??
In some cases, it replaces it with closely similar letters such as /v/ and
/w/. In other cases, it completely removes it. That is what is happening
with my case. Those characters re sometimes completely removed; other
times, they are replaced by closely resembling characters. Because of this
inconsistency, applying regex is very difficult.
On Wednesday, September 13, 2023 at 1:02:01 PM UTC+3 mdalihu...@gmail.com
wrote:
> if Some specific characters or words are always missing from the OCR
> result. then you can apply logic with the Regular expressions method on
> your applications. After OCR, these specific characters or words will be
> replaced by current characters or words that you defined in your
> applications by Regular expressions. it can be done in some major problems.
>
> On Wednesday, 13 September, 2023 at 3:51:29 pm UTC+6 desal...@gmail.com
> wrote:
>
>> The characters are getting missed, even after fine-tuning.
>> I never made any progress. I tried many different ways. Some specific
>> characters are always missing from the OCR result.
>>
>> On Wednesday, September 13, 2023 at 12:49:20 PM UTC+3
>> mdalihu...@gmail.com wrote:
>>
>>> EasyOCR I think is best for ID cards or something like that image
>>> process. but document images like books, here Tesseract is better than
>>> EasyOCR. Even I didn't use EasyOCR. you can try it.
>>>
>>> I have added words of dictionaries but the result is the same.
>>>
>>> what kind of problem you have faced in fine-tuning in few new characters
>>> as you said (*but, I failed in every possible way to introduce a few
>>> new characters into the database.)*
>>> On Wednesday, 13 September, 2023 at 3:33:48 pm UTC+6 desal...@gmail.com
>>> wrote:
>>>
>>>> Yes, we are new to this. I find the instructions (the manual) very hard
>>>> to follow. The video you linked above was really helpful to get started.
>>>> My plan at the beginning was to fine tune the existing .traineddata. But,
>>>> I
>>>> failed in every possible way to introduce a few new characters into the
>>>> database. That is why I started from scratch.
>>>>
>>>> Sure, I will follow Lorenzo's suggestion: will run more the iterations,
>>>> and see if I can improve.
>>>>
>>>> Another areas we need to explore is usage of dictionaries actually. May
>>>> be adding millions of words into the dictionary could help Tesseract. I
>>>> don't have millions of words; but I am looking into some corpus to get
>>>> more
>>>> words into the dictionary.
>>>>
>>>> If this all fails, EasyOCR (and probably other similar open-source
>>>> packages) is probably our next option to try on. Sure, sharing
>>>> our experiences will be helpful. I will let you know if I made good
>>>> progresses in any of these options.
>>>> On Wednesday, September 13, 2023 at 12:19:48 PM UTC+3
>>>> mdalihu...@gmail.com wrote:
>>>>
>>>>> How is your training going for Bengali? It was nearly good but I
>>>>> faced space problems between two words, some words are spaces but most of
>>>>> them have no space. I think is problem is in the dataset but I use the
>>>>> default training dataset from Tesseract which is used in Ben That way I
>>>>> am
>>>>> confused so I have to explore more. by the way, you can try as Lorenzo
>>>>> Blz said. Actually training from scratch is harder than fine-tuning.
>>>>> so you can use different datasets to explore. if you succeed. please let
>>>>> me
>>>>> know how you have done this whole process. I'm also new in this field.
>>>>> On Wednesday, 13 September, 2023 at 1:13:43 pm UTC+6
>>>>> desal...@gmail.com wrote:
>>>>>
>>>>>> How is your training going for Bengali?
>>>>>> I have been trying to train from scratch. I made about 64,000 lines
>>>>>> of text (which produced about 255,000 files, in the end) and run the
>>>>>> training for 150,000 iterations; getting 0.51 training error rate. I was
>>>>>> hopping to get reasonable accuracy. Unfortunately, when I run the OCR
>>>>>> using .traineddata, the accuracy is absolutely terrible. Do you think
>>>>>> I
>>>>>> made some mistakes, or that is an expected result?
>>>>>>
>>>>>> On Tuesday, September 12, 2023 at 11:15:25 PM UTC+3
>>>>>> mdalihu...@gmail.com wrote:
>>>>>>
>>>>>>> Yes, he doesn't mention all fonts but only one font. That way he
>>>>>>> didn't use *MODEL_NAME in a separate **script **file script I
>>>>>>> think.*
>>>>>>>
>>>>>>> Actually, here we teach all *tif, gt.txt, and .box files *which are
>>>>>>> created by *MODEL_NAME I mean **eng, ben, oro flag or language
>>>>>>> code *because when we first create *tif, gt.txt, and .box files, *every
>>>>>>> file starts by *MODEL_NAME*. This *MODEL_NAME* we selected on
>>>>>>> the training script for looping each tif, gt.txt, and .box files which
>>>>>>> are
>>>>>>> created by *MODEL_NAME.*
>>>>>>>
>>>>>>> On Tuesday, 12 September, 2023 at 9:42:13 pm UTC+6
>>>>>>> desal...@gmail.com wrote:
>>>>>>>
>>>>>>>> Yes, I am familiar with the video and have set up the folder
>>>>>>>> structure as you did. Indeed, I have tried a number of fine-tuning
>>>>>>>> with a
>>>>>>>> single font following Gracia's video. But, your script is much better
>>>>>>>> because supports multiple fonts. The whole improvement you made is
>>>>>>>> brilliant; and very useful. It is all working for me.
>>>>>>>> The only part that I didn't understand is the trick you used in
>>>>>>>> your tesseract_train.py script. You see, I have been doing exactly to
>>>>>>>> you
>>>>>>>> did except this script.
>>>>>>>>
>>>>>>>> The scripts seems to have the trick of sending/teaching each of the
>>>>>>>> fonts (iteratively) into the model. The script I have been using
>>>>>>>> (which I
>>>>>>>> get from Garcia) doesn't mention font at all.
>>>>>>>>
>>>>>>>> *TESSDATA_PREFIX=../tesseract/tessdata make training MODEL_NAME=oro
>>>>>>>> TESSDATA=../tesseract/tessdata MAX_ITERATIONS=10000*
>>>>>>>> Does it mean that my model does't train the fonts (even if the
>>>>>>>> fonts have been included in the splitting process, in the other
>>>>>>>> script)?
>>>>>>>> On Monday, September 11, 2023 at 10:54:08 AM UTC+3
>>>>>>>> mdalihu...@gmail.com wrote:
>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> *import subprocess# List of font namesfont_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"*
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> * subprocess.run(command, shell=True) 1 . This command is for
>>>>>>>>> training data that I have named '*tesseract_training*.py' inside
>>>>>>>>> tesstrain folder.*
>>>>>>>>> *2. root directory means your main training folder and inside it
>>>>>>>>> as like langdata, tessearact, tesstrain folders. if you see this
>>>>>>>>> tutorial
>>>>>>>>> *https://www.youtube.com/watch?v=KE4xEzFGSU8 you will
>>>>>>>>> understand better the folder structure. only I
>>>>>>>>> created tesseract_training.py in tesstrain folder for training and
>>>>>>>>> FontList.py file is the main path as *like langdata, tessearact,
>>>>>>>>> tesstrain, and *split_training_text.py.
>>>>>>>>> 3. first of all you have to put all fonts in your Linux fonts
>>>>>>>>> folder. /usr/share/fonts/ then run: sudo apt update then sudo
>>>>>>>>> fc-cache -fv
>>>>>>>>>
>>>>>>>>> after that, you have to add the exact font's name in FontList.py
>>>>>>>>> file like me.
>>>>>>>>> I have added two pic my folder structure. first is main
>>>>>>>>> structure pic and the second is the Colopse tesstrain folder.
>>>>>>>>>
>>>>>>>>> I[image: Screenshot 2023-09-11 134947.png][image: Screenshot
>>>>>>>>> 2023-09-11 135014.png]
>>>>>>>>> On Monday, 11 September, 2023 at 12:50:03 pm UTC+6
>>>>>>>>> desal...@gmail.com wrote:
>>>>>>>>>
>>>>>>>>>> Thank you so much for putting out these brilliant scripts. They
>>>>>>>>>> make the process much more efficient.
>>>>>>>>>>
>>>>>>>>>> I have one more question on the other script that you use to
>>>>>>>>>> train.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> *import subprocess# List of font namesfont_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"*
>>>>>>>>>> * subprocess.run(command, shell=True) *
>>>>>>>>>>
>>>>>>>>>> Do you have the name of fonts listed in file in the same/root
>>>>>>>>>> directory?
>>>>>>>>>> How do you setup the names of the fonts in the file, if you don't
>>>>>>>>>> mind sharing it?
>>>>>>>>>> On Monday, September 11, 2023 at 4:27:27 AM UTC+3
>>>>>>>>>> mdalihu...@gmail.com wrote:
>>>>>>>>>>
>>>>>>>>>>> You can use the new script below. it's better than the previous
>>>>>>>>>>> two scripts. You can create *tif, gt.txt, and .box files *by
>>>>>>>>>>> multiple fonts and also use breakpoint if vs code close or anything
>>>>>>>>>>> during
>>>>>>>>>>> creating *tif, gt.txt, and .box files *then you can checkpoint
>>>>>>>>>>> to navigate where you close vs code.
>>>>>>>>>>>
>>>>>>>>>>> command for *tif, gt.txt, and .box files *
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> import os
>>>>>>>>>>> import random
>>>>>>>>>>> import pathlib
>>>>>>>>>>> import subprocess
>>>>>>>>>>> import argparse
>>>>>>>>>>> from FontList import FontList
>>>>>>>>>>>
>>>>>>>>>>> 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:
>>>>>>>>>>> lines = input_file.readlines()
>>>>>>>>>>>
>>>>>>>>>>> if not os.path.exists(output_directory):
>>>>>>>>>>> os.mkdir(output_directory)
>>>>>>>>>>>
>>>>>>>>>>> if start_line is None:
>>>>>>>>>>> start_line = 0
>>>>>>>>>>>
>>>>>>>>>>> if end_line is None:
>>>>>>>>>>> end_line = len(lines) - 1
>>>>>>>>>>>
>>>>>>>>>>> for font_name in font_list.fonts:
>>>>>>>>>>> for line_index in range(start_line, end_line + 1):
>>>>>>>>>>> line = lines[line_index].strip()
>>>>>>>>>>>
>>>>>>>>>>> training_text_file_name = pathlib.Path(
>>>>>>>>>>> training_text_file).stem
>>>>>>>>>>>
>>>>>>>>>>> line_serial = f"{line_index:d}"
>>>>>>>>>>>
>>>>>>>>>>> line_gt_text = os.path.join(output_directory, f'{
>>>>>>>>>>> training_text_file_name}_{line_serial}_{font_name.replace(" ", "
>>>>>>>>>>> _")}.gt.txt')
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> with open(line_gt_text, 'w') as output_file:
>>>>>>>>>>> output_file.writelines([line])
>>>>>>>>>>>
>>>>>>>>>>> file_base_name = f'{training_text_file_name}_{
>>>>>>>>>>> line_serial}_{font_name.replace(" ", "_")}'
>>>>>>>>>>> subprocess.run([
>>>>>>>>>>> 'text2image',
>>>>>>>>>>> f'--font={font_name}',
>>>>>>>>>>> f'--text={line_gt_text}',
>>>>>>>>>>> f'--outputbase={output_directory}/{
>>>>>>>>>>> file_base_name}',
>>>>>>>>>>> '--max_pages=1',
>>>>>>>>>>> '--strip_unrenderable_words',
>>>>>>>>>>> '--leading=36',
>>>>>>>>>>> '--xsize=3600',
>>>>>>>>>>> '--ysize=330',
>>>>>>>>>>> '--char_spacing=1.0',
>>>>>>>>>>> '--exposure=0',
>>>>>>>>>>> '--unicharset_file=langdata/eng.unicharset',
>>>>>>>>>>> ])
>>>>>>>>>>>
>>>>>>>>>>> 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/eng.training_text'
>>>>>>>>>>> output_directory = 'tesstrain/data/eng-ground-truth'
>>>>>>>>>>>
>>>>>>>>>>> font_list = FontList()
>>>>>>>>>>>
>>>>>>>>>>> create_training_data(training_text_file, font_list,
>>>>>>>>>>> output_directory, args.start, args.end)
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Then create a file called "FontList" in the root directory and
>>>>>>>>>>> paste it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> class FontList:
>>>>>>>>>>> def __init__(self):
>>>>>>>>>>> self.fonts = [
>>>>>>>>>>> "Gerlick"
>>>>>>>>>>> "Sagar Medium",
>>>>>>>>>>> "Ekushey Lohit Normal",
>>>>>>>>>>> "Charukola Round Head Regular, weight=433",
>>>>>>>>>>> "Charukola Round Head Bold, weight=443",
>>>>>>>>>>> "Ador Orjoma Unicode",
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> ]
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> then import in the above code,
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> *for breakpoint command:*
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> sudo python3 split_training_text.py --start 0 --end 11
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> change checkpoint according to you --start 0 --end 11.
>>>>>>>>>>>
>>>>>>>>>>> *and training checkpoint as you know already.*
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Monday, 11 September, 2023 at 1:22:34 am UTC+6
>>>>>>>>>>> desal...@gmail.com wrote:
>>>>>>>>>>>
>>>>>>>>>>>> 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
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>> You received this message because you are subscribed to the
>>>>>>>>>>>>>>> Google Groups "tesseract-ocr" group.
>>>>>>>>>>>>>>> To unsubscribe from this group and stop receiving emails
>>>>>>>>>>>>>>> from it, send an email to tesseract-oc...@googlegroups.com.
>>>>>>>>>>>>>>> To view this discussion on the web visit
>>>>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/406cd733-b265-4118-a7ca-de75871cac39n%40googlegroups.com
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/406cd733-b265-4118-a7ca-de75871cac39n%40googlegroups.com?utm_medium=email&utm_source=footer>
>>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
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