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.
