try with another text dataset. if I am not wrong I think because Ray has
not updated the training text dataset for many years and which trained data
now are available these are from another dataset.
On Thursday, 14 September, 2023 at 12:19:53 am UTC+6 desal...@gmail.com
wrote:
> I now get to 200000 iterations; and the error rate is stuck at 0.46. The
> result is absolutely trash: nowhere close to the default/Ray's training.
>
> On Wednesday, September 13, 2023 at 2:47:05 PM UTC+3 mdalihu...@gmail.com
> wrote:
>
>>
>> after Tesseact recognizes text from images. then you can apply regex to
>> replace the wrong word with to correct word.
>> I'm not familiar with paddleOcr and scanTailor also.
>>
>> On Wednesday, 13 September, 2023 at 5:06:12 pm UTC+6 desal...@gmail.com
>> wrote:
>>
>>> At what stage are you doing the regex replacement?
>>> My process has been: Scan (tif)--> ScanTailor --> Tesseract --> pdf
>>>
>>> >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.
>>>
>>> How about paddleOcr?, are you familiar with it?
>>>
>>> On Wednesday, September 13, 2023 at 1:45:54 PM UTC+3
>>> mdalihu...@gmail.com wrote:
>>>
>>>> I know what you mean. but in some cases, it helps me. I have faced
>>>> specific characters and words are always not recognized by Tesseract. That
>>>> way I use these regex to replace those characters and words if those
>>>> characters are incorrect.
>>>>
>>>> see what I have done:
>>>>
>>>> " ী": "ী",
>>>> " ্": " ",
>>>> " ে": " ",
>>>> জ্া: "জা",
>>>> " ": " ",
>>>> " ": " ",
>>>> " ": " ",
>>>> "্প": " ",
>>>> " য": "র্য",
>>>> য: "য",
>>>> " া": "া",
>>>> আা: "আ",
>>>> ম্ি: "মি",
>>>> স্ু: "সু",
>>>> "হূ ": "হূ",
>>>> " ণ": "ণ",
>>>> র্্: "র",
>>>> "চিন্ত ": "চিন্তা ",
>>>> ন্া: "না",
>>>> "সম ূর্ন": "সম্পূর্ণ",
>>>> On Wednesday, 13 September, 2023 at 4:18:22 pm UTC+6 desal...@gmail.com
>>>> wrote:
>>>>
>>>>> 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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