Is there any way to pass bounding boxes to use to the LSTM? We have an algorithm that cleanly gets bounding boxes of MRZ characters. However the results using psm 10 are worse than passing the whole line in. Yet when we pass the whole line in we get these phantom characters.
Should PSM 10 mode work? It often returns “no character” where there clearly is one. I can supply a test case if it is expected to work well. On Fri, Jul 19, 2019 at 11:06 AM ElGato ElMago <[email protected]> wrote: > Lorenzo, > > We both have got the same case. It seems a solution to this problem would > save a lot of people. > > Shree, > > I pulled the current head of master branch but it doesn't seem to contain > the merges you pointed that have been merged 3 to 4 days ago. How can I > get them? > > ElMagoElGato > > 2019年7月19日金曜日 17時02分53秒 UTC+9 Lorenzo Blz: >> >> >> >> PSM 7 was a partial solution for my specific case, it improved the >> situation but did not solve it. Also I could not use it in some other cases. >> >> The proper solution is very likely doing more training with more data, >> some data augmentation might probably help if data is scarce. >> Also doing less training might help is the training is not done correctly. >> >> There are also similar issues on github: >> >> https://github.com/tesseract-ocr/tesseract/issues/1465 >> ... >> >> The LSTM engine works like this: it scans the image and for each "pixel >> column" does this: >> >> M M M M N M M M [BLANK] F F F F >> >> (here i report only the highest probability characters) >> >> In the example above an M is partially seen as an N, this is normal, and >> another step of the algorithm (beam search I think) tries to aggregate back >> the correct characters. >> >> I think cases like this: >> >> M M M N N N M M >> >> are what gives the phantom characters. More training should reduce the >> source of the problem or a painful analysis of the bounding boxes might fix >> some cases. >> >> >> I used the attached script for the boxes. >> >> >> Lorenzo >> >> >> >> >> Il giorno ven 19 lug 2019 alle ore 07:25 ElGato ElMago < >> [email protected]> ha scritto: >> > Hi, >>> >>> Let's call them phantom characters then. >>> >>> Was psm 7 the solution for the issue 1778? None of the psm option >>> didn't solve my problem though I see different output. >>> >>> I use tesseract 5.0-alpha mostly but 4.1 showed the same results >>> anyway. How did you get bounding box for each character? Alto and lstmbox >>> only show bbox for a group of characters. >>> >>> ElMagoElGato >>> >>> 2019年7月17日水曜日 18時58分31秒 UTC+9 Lorenzo Blz: >>> >>>> Phantom characters here for me too: >>>> >>>> https://github.com/tesseract-ocr/tesseract/issues/1778 >>>> >>>> Are you using 4.1? Bounding boxes were fixed in 4.1 maybe this was also >>>> improved. >>>> >>>> I wrote some code that uses symbols iterator to discard symbols that >>>> are clearly duplicated: too small, overlapping, etc. But it was not easy to >>>> make it work decently and it is not 100% reliable with false negatives and >>>> positives. I cannot share the code and it is quite ugly anyway. >>>> >>>> Here there is another MRZ model with training data: >>>> >>>> https://github.com/DoubangoTelecom/tesseractMRZ >>>> >>>> >>>> >>>> >>>> Lorenzo >>>> >>>> >>>> Il giorno mer 17 lug 2019 alle ore 11:26 Claudiu <[email protected]> >>>> ha scritto: >>>> >>>>> I’m getting the “phantom character” issue as well using the OCRB that >>>>> Shree trained on MRZ lines. For example for a 0 it will sometimes add both >>>>> a 0 and an O to the output , thus outputting 45 characters total instead >>>>> of >>>>> 44. I haven’t looked at the bounding box output yet but I suspect a >>>>> phantom >>>>> thin character is added somewhere that I can discard .. or maybe two chars >>>>> will have the same bounding box. If anyone else has fixed this issue >>>>> further up (eg so the output doesn’t contain the phantom characters in the >>>>> first place) id be interested. >>>>> >>>>> On Wed, Jul 17, 2019 at 10:01 AM ElGato ElMago <[email protected]> >>>>> wrote: >>>>> >>>>>> Hi, >>>>>> >>>>>> I'll go back to more of training later. Before doing so, I'd like to >>>>>> investigate results a little bit. The hocr and lstmbox options give some >>>>>> details of positions of characters. The results show positions that >>>>>> perfectly correspond to letters in the image. But the text output >>>>>> contains >>>>>> a character that obviously does not exist. >>>>>> >>>>>> Then I found a config file 'lstmdebug' that generates far more >>>>>> information. I hope it explains what happened with each character. I'm >>>>>> yet to read the debug output but I'd appreciate it if someone could tell >>>>>> me >>>>>> how to read it because it's really complex. >>>>>> >>>>>> Regards, >>>>>> ElMagoElGato >>>>>> >>>>>> 2019年6月14日金曜日 19時58分49秒 UTC+9 shree: >>>>>> >>>>>>> See https://github.com/Shreeshrii/tessdata_MICR >>>>>>> >>>>>>> I have uploaded my files there. >>>>>>> >>>>>>> https://github.com/Shreeshrii/tessdata_MICR/blob/master/MICR.sh >>>>>>> is the bash script that runs the training. >>>>>>> >>>>>>> You can modify as needed. Please note this is for legacy/base >>>>>>> tesseract --oem 0. >>>>>>> >>>>>>> On Fri, Jun 14, 2019 at 1:26 PM ElGato ElMago <[email protected]> >>>>>>> wrote: >>>>>>> >>>>>>>> Thanks a lot, shree. It seems you know everything. >>>>>>>> >>>>>>>> I tried the MICR0.traineddata and the first two mcr.traineddata. >>>>>>>> The last one was blocked by the browser. Each of the traineddata had >>>>>>>> mixed >>>>>>>> results. All of them are getting symbols fairly good but getting >>>>>>>> spaces >>>>>>>> randomly and reading some numbers wrong. >>>>>>>> >>>>>>>> MICR0 seems the best among them. Did you suggest that you'd be >>>>>>>> able to update it? It gets tripple D very often where there's only >>>>>>>> one, >>>>>>>> and so on. >>>>>>>> >>>>>>>> Also, I tried to fine tune from MICR0 but I found that I need to >>>>>>>> change the language-specific.sh. It specifies some parameters for each >>>>>>>> language. Do you have any guidance for it? >>>>>>>> >>>>>>>> 2019年6月14日金曜日 1時48分40秒 UTC+9 shree: >>>>>>>>> >>>>>>>>> see >>>>>>>>> http://www.devscope.net/Content/ocrchecks.aspx >>>>>>>>> https://github.com/BigPino67/Tesseract-MICR-OCR >>>>>>>>> >>>>>>>>> https://groups.google.com/d/msg/tesseract-ocr/obWI4cz8rXg/6l82hEySgOgJ >>>>>>>>> >>>>>>>>> >>>>>>>>> On Mon, Jun 10, 2019 at 11:21 AM ElGato ElMago < >>>>>>>>> [email protected]> wrote: >>>>>>>>> >>>>>>>>>> That'll be nice if there's traineddata out there but I didn't >>>>>>>>>> find any. I see free fonts and commercial OCR software but not >>>>>>>>>> traineddata. Tessdata repository obviously doesn't have one, either. >>>>>>>>>> >>>>>>>>>> 2019年6月8日土曜日 1時52分10秒 UTC+9 shree: >>>>>>>>>>> >>>>>>>>>>> Please also search for existing MICR traineddata files. >>>>>>>>>>> >>>>>>>>>>> On Thu, Jun 6, 2019 at 1:09 PM ElGato ElMago < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> So I did several tests from scratch. In the last attempt, I >>>>>>>>>>>> made a training text with 4,000 lines in the following format, >>>>>>>>>>>> >>>>>>>>>>>> 110004310510< <02 :4002=0181:801= 0008752 <00039 ;0000001000; >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> and combined it with eng.digits.training_text in which symbols >>>>>>>>>>>> are converted to E13B symbols. This makes about 12,000 lines of >>>>>>>>>>>> training >>>>>>>>>>>> text. It's amazing that this thing generates a good reader out of >>>>>>>>>>>> nowhere. But then it is not very good. For example: >>>>>>>>>>>> >>>>>>>>>>>> <01 :1901=1386:021= 1111001<10001< ;0000090134; >>>>>>>>>>>> >>>>>>>>>>>> is a result on the image attached. It's close but the last '<' >>>>>>>>>>>> in the result text doesn't exist on the image. It's a small >>>>>>>>>>>> failure but it >>>>>>>>>>>> causes a greater trouble in parsing. >>>>>>>>>>>> >>>>>>>>>>>> What would you suggest from here to increase accuracy? >>>>>>>>>>>> >>>>>>>>>>>> - Increase the number of lines in the training text >>>>>>>>>>>> - Mix up more variations in the training text >>>>>>>>>>>> - Increase the number of iterations >>>>>>>>>>>> - Investigate wrong reads one by one >>>>>>>>>>>> - Or else? >>>>>>>>>>>> >>>>>>>>>>>> Also, I referred to engrestrict*.* and could generate similar >>>>>>>>>>>> result with the fine-tuning-from-full method. It seems a bit >>>>>>>>>>>> faster to get >>>>>>>>>>>> to the same level but it also stops at a 'good' level. I can go >>>>>>>>>>>> with >>>>>>>>>>>> either way if it takes me to the bright future. >>>>>>>>>>>> >>>>>>>>>>>> Regards, >>>>>>>>>>>> ElMagoElGato >>>>>>>>>>>> >>>>>>>>>>>> 2019年5月30日木曜日 15時56分02秒 UTC+9 ElGato ElMago: >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks a lot, Shree. I'll look it in. >>>>>>>>>>>>> >>>>>>>>>>>>> 2019年5月30日木曜日 14時39分52秒 UTC+9 shree: >>>>>>>>>>>>>> >>>>>>>>>>>>>> See https://github.com/Shreeshrii/tessdata_shreetest >>>>>>>>>>>>>> >>>>>>>>>>>>>> Look at the files engrestrict*.* and also >>>>>>>>>>>>>> https://github.com/Shreeshrii/tessdata_shreetest/blob/master/eng.digits.training_text >>>>>>>>>>>>>> >>>>>>>>>>>>>> Create training text of about 100 lines and finetune for 400 >>>>>>>>>>>>>> lines >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Thu, May 30, 2019 at 9:38 AM ElGato ElMago < >>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> I had about 14 lines as attached. How many lines would you >>>>>>>>>>>>>>> recommend? >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Fine tuning gives much better result but it tends to pick >>>>>>>>>>>>>>> other character than in E13B that only has 14 characters, 0 >>>>>>>>>>>>>>> through 9 and 4 >>>>>>>>>>>>>>> symbols. I thought training from scratch would eliminate such >>>>>>>>>>>>>>> confusion. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> 2019年5月30日木曜日 10時43分08秒 UTC+9 shree: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> For training from scratch a large training text and >>>>>>>>>>>>>>>> hundreds of thousands of iterations are recommended. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> If you are just fine tuning for a font try to follow >>>>>>>>>>>>>>>> instructions for training for impact, with your font. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Thu, 30 May 2019, 06:05 ElGato ElMago, < >>>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Thanks, Shree. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Yes, I saw the instruction. The steps I made are as >>>>>>>>>>>>>>>>> follows: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Using tesstrain.sh: >>>>>>>>>>>>>>>>> src/training/tesstrain.sh --fonts_dir /usr/share/fonts >>>>>>>>>>>>>>>>> --lang eng --linedata_only \ >>>>>>>>>>>>>>>>> --noextract_font_properties --langdata_dir ../langdata \ >>>>>>>>>>>>>>>>> --tessdata_dir ./tessdata \ >>>>>>>>>>>>>>>>> --fontlist "E13Bnsd" --output_dir >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval \ >>>>>>>>>>>>>>>>> --training_text ../langdata/eng/eng.training_e13b_text >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Training from scratch: >>>>>>>>>>>>>>>>> mkdir -p ~/tesstutorial/e13boutput >>>>>>>>>>>>>>>>> src/training/lstmtraining --debug_interval 100 \ >>>>>>>>>>>>>>>>> --traineddata >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng/eng.traineddata \ >>>>>>>>>>>>>>>>> --net_spec '[1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 Lfx96 Lrx96 >>>>>>>>>>>>>>>>> Lfx256 O1c111]' \ >>>>>>>>>>>>>>>>> --model_output ~/tesstutorial/e13boutput/base >>>>>>>>>>>>>>>>> --learning_rate 20e-4 \ >>>>>>>>>>>>>>>>> --train_listfile >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng.training_files.txt \ >>>>>>>>>>>>>>>>> --eval_listfile >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng.training_files.txt \ >>>>>>>>>>>>>>>>> --max_iterations 5000 >>>>>>>>>>>>>>>>> &>~/tesstutorial/e13boutput/basetrain.log >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Test with base_checkpoint: >>>>>>>>>>>>>>>>> src/training/lstmeval --model >>>>>>>>>>>>>>>>> ~/tesstutorial/e13boutput/base_checkpoint \ >>>>>>>>>>>>>>>>> --traineddata >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng/eng.traineddata \ >>>>>>>>>>>>>>>>> --eval_listfile >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng.training_files.txt >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Combining output files: >>>>>>>>>>>>>>>>> src/training/lstmtraining --stop_training \ >>>>>>>>>>>>>>>>> --continue_from >>>>>>>>>>>>>>>>> ~/tesstutorial/e13boutput/base_checkpoint \ >>>>>>>>>>>>>>>>> --traineddata >>>>>>>>>>>>>>>>> ~/tesstutorial/e13beval/eng/eng.traineddata \ >>>>>>>>>>>>>>>>> --model_output ~/tesstutorial/e13boutput/eng.traineddata >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Test with eng.traineddata: >>>>>>>>>>>>>>>>> tesseract e13b.png out --tessdata-dir >>>>>>>>>>>>>>>>> /home/koichi/tesstutorial/e13boutput >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> The training from scratch ended as: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> At iteration 561/2500/2500, Mean rms=0.159%, delta=0%, >>>>>>>>>>>>>>>>> char train=0%, word train=0%, skip ratio=0%, New best char >>>>>>>>>>>>>>>>> error = 0 wrote >>>>>>>>>>>>>>>>> best >>>>>>>>>>>>>>>>> model:/home/koichi/tesstutorial/e13boutput/base0_561.checkpoint >>>>>>>>>>>>>>>>> wrote >>>>>>>>>>>>>>>>> checkpoint. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> The test with base_checkpoint returns nothing as: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> At iteration 0, stage 0, Eval Char error rate=0, Word >>>>>>>>>>>>>>>>> error rate=0 >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> The test with eng.traineddata and e13b.png returns >>>>>>>>>>>>>>>>> out.txt. Both files are attached. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Training seems to have worked fine. I don't know how to >>>>>>>>>>>>>>>>> translate the test result from base_checkpoint. The generated >>>>>>>>>>>>>>>>> eng.traineddata obviously doesn't work well. I suspect the >>>>>>>>>>>>>>>>> choice of >>>>>>>>>>>>>>>>> --traineddata in combining output files is bad but I have no >>>>>>>>>>>>>>>>> clue. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Regards, >>>>>>>>>>>>>>>>> ElMagoElGato >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> BTW, I referred to your tess4training in the process. It >>>>>>>>>>>>>>>>> helped a lot. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> 2019年5月29日水曜日 19時14分08秒 UTC+9 shree: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> see >>>>>>>>>>>>>>>>>> https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#combining-the-output-files >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On Wed, May 29, 2019 at 3:18 PM ElGato ElMago < >>>>>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> I wish to make a trained data for E13B font. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> I read the training tutorial and made a base_checkpoint >>>>>>>>>>>>>>>>>>> file according to the method in Training From Scratch. >>>>>>>>>>>>>>>>>>> Now, how can I make >>>>>>>>>>>>>>>>>>> a trained data from the base_checkpoint file? >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>>>>>> 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 post to this group, send email to >>>>>>>>>>>>>>>>>>> [email protected]. >>>>>>>>>>>>>>>>>>> Visit this group at >>>>>>>>>>>>>>>>>>> https://groups.google.com/group/tesseract-ocr. >>>>>>>>>>>>>>>>>>> To view this discussion on the web visit >>>>>>>>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/4848cfa5-ae2b-4be3-a771-686aa0fec702%40googlegroups.com >>>>>>>>>>>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/4848cfa5-ae2b-4be3-a771-686aa0fec702%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>>>>>>>>>>>>>> . >>>>>>>>>>>>>>>>>>> For more options, visit >>>>>>>>>>>>>>>>>>> https://groups.google.com/d/optout. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> ____________________________________________________________ >>>>>>>>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>>>> 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 post to this group, send email to >>>>>>>>>>>>>>>>> [email protected]. >>>>>>>>>>>>>>>>> Visit this group at >>>>>>>>>>>>>>>>> https://groups.google.com/group/tesseract-ocr. >>>>>>>>>>>>>>>>> To view this discussion on the web visit >>>>>>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/7f29f47e-c6f5-4743-832d-94e7d28ab4e8%40googlegroups.com >>>>>>>>>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/7f29f47e-c6f5-4743-832d-94e7d28ab4e8%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>>>>>>>>>>>> . >>>>>>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout >>>>>>>>>>>>>>>>> . >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>> 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 post to this group, send email to >>>>>>>>>>>>>>> [email protected]. >>>>>>>>>>>>>>> Visit this group at >>>>>>>>>>>>>>> https://groups.google.com/group/tesseract-ocr. >>>>>>>>>>>>>>> To view this discussion on the web visit >>>>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/2c6fe865-911d-41f3-9926-cbfb56db794f%40googlegroups.com >>>>>>>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/2c6fe865-911d-41f3-9926-cbfb56db794f%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>>>>>>>>>> . >>>>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>>>> >>>>>>>>>>>>>> ____________________________________________________________ >>>>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com >>>>>>>>>>>>>> >>>>>>>>>>>>> -- >>>>>>>>>>>> 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 post to this group, send email to [email protected] >>>>>>>>>>>> . >>>>>>>>>>>> Visit this group at >>>>>>>>>>>> https://groups.google.com/group/tesseract-ocr. >>>>>>>>>>>> To view this discussion on the web visit >>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/5b151e61-5b41-4191-8d26-784809ef8e10%40googlegroups.com >>>>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/5b151e61-5b41-4191-8d26-784809ef8e10%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>>>>>>> . >>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> >>>>>>>>>>> ____________________________________________________________ >>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com >>>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> 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 post to this group, send email to [email protected]. >>>>>>>>>> Visit this group at https://groups.google.com/group/tesseract-ocr >>>>>>>>>> . >>>>>>>>>> To view this discussion on the web visit >>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/09d3119c-d093-4269-bf3a-3ddb467ed0ed%40googlegroups.com >>>>>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/09d3119c-d093-4269-bf3a-3ddb467ed0ed%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>>>>> . >>>>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> >>>>>>>>> ____________________________________________________________ >>>>>>>>> भजन - 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