Lorenzo, I haven't been checking psm too much. Will turn to those options after I see how it goes with bounding boxes.
Shree, I see the merges in the git log and also see that new option lstm_choice_amount works now. I guess my executable is latest though I still see the phantom character. Hocr makes huge and complex output. I'll take some to read it. 2019年7月19日金曜日 18時20分55秒 UTC+9 Claudiu: > > 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 <elmago...@gmail.com > <javascript:>> 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 < >>> elmago...@gmail.com> 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 <csaf...@gmail.com> >>>>> 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 <elmago...@gmail.com> >>>>>> 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 <elmago...@gmail.com> >>>>>>>> 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 < >>>>>>>>>> elmago...@gmail.com> 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 < >>>>>>>>>>>> elmago...@gmail.com> 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 < >>>>>>>>>>>>>>> elmago...@gmail.com> 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, < >>>>>>>>>>>>>>>>> elmago...@gmail.com> 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 < >>>>>>>>>>>>>>>>>>> elmago...@gmail.com> 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 >>>>>>>>>>>>>>>>>>>> tesser...@googlegroups.com. >>>>>>>>>>>>>>>>>>>> To post to this group, send email to >>>>>>>>>>>>>>>>>>>> tesser...@googlegroups.com. >>>>>>>>>>>>>>>>>>>> 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 tesser...@googlegroups.com. >>>>>>>>>>>>>>>>>> To post to this group, send email to >>>>>>>>>>>>>>>>>> tesser...@googlegroups.com. >>>>>>>>>>>>>>>>>> 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 tesser...@googlegroups.com. >>>>>>>>>>>>>>>> To post to this group, send email to >>>>>>>>>>>>>>>> tesser...@googlegroups.com. >>>>>>>>>>>>>>>> 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 tesser...@googlegroups.com. >>>>>>>>>>>>> To post to this group, send email to >>>>>>>>>>>>> tesser...@googlegroups.com. >>>>>>>>>>>>> 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 tesser...@googlegroups.com. >>>>>>>>>>> To post to this group, send email to tesser...@googlegroups.com. >>>>>>>>>>> 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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