See https://github.com/tesseract-ocr/tesseract/issues/2580
On Tue, 23 Jul 2019, 06:23 ElGato ElMago, <elmagoelg...@gmail.com> wrote: > Hi, > > I read the output of hocr with lstm_choice_mode = 4 as to the pull request > 2554. It shows the candidates for each character but doesn't show bounding > box of each character. I only shows the box for a whole word. > > I see bounding boxes of each character in comments of the pull request > 2576. How can I do that? Do I have to look in the source code and > manipulate such an output on my own? > > 2019年7月19日金曜日 18時40分49秒 UTC+9 ElGato ElMago: > >> 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> >>> 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 <a href=" >>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/09d3119c-d093-4269-bf3a-3ddb467ed0ed%40googlegroups.com?utm_medium=email&utm_source=footer"; >>>>>>>>>>>>> rel="nofollow" target="_blank" onmousedown="this.href= >>>>>>>>>>>> >>>>>>>>>>>> -- > 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-ocr+unsubscr...@googlegroups.com. > To view this discussion on the web visit > https://groups.google.com/d/msgid/tesseract-ocr/c02fd92c-21fe-48a0-a281-a2c01f5332ca%40googlegroups.com > <https://groups.google.com/d/msgid/tesseract-ocr/c02fd92c-21fe-48a0-a281-a2c01f5332ca%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- 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-ocr+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/tesseract-ocr/CAG2NduUK5yo0ezYPAbZLHMVcGFQ8kBG6kKEro%3DgmpsB_ajKfiA%40mail.gmail.com.
