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 [email protected] 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 [email protected] > 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 [email protected] >> 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 >>> [email protected] 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 [email protected] >>>> 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 >>>>> [email protected] 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 [email protected] >>>>>> 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 >>>>>>> [email protected] 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 >>>>>>>> [email protected] 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 >>>>>>>>> [email protected] 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 >>>>>>>>>> [email protected] 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 >>>>>>>>>>> [email protected] 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 <[email protected]> >>>>>>>>>>>>> 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 [email protected]. >>>>>>>>>>>>>> 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> >>>>>>>>>>>>>> . >>>>>>>>>>>>>> >>>>>>>>>>>>> -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. 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