It's great! Perfect! Thanks a lot! 2019年7月23日火曜日 10時56分58秒 UTC+9 shree: > > See https://github.com/tesseract-ocr/tesseract/issues/2580 > > On Tue, 23 Jul 2019, 06:23 ElGato ElMago, <elmago...@gmail.com > <javascript:>> 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 - 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