Thanks for that link. Should passing a full image and calling SetRectangle
have different behavior than passing just the cropped image and not using
SetRectangle?

On Fri, Jul 19, 2019 at 11:29 AM Shree Devi Kumar <shreesh...@gmail.com>
wrote:

> >Is there any way to pass bounding boxes to use to the LSTM?
>
> See
> https://github.com/tesseract-ocr/tesseract/wiki/APIExample#getcomponentimages-example
>
>
> On Fri, Jul 19, 2019 at 2:50 PM Claudiu <csaft...@gmail.com> wrote:
>
>> 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 <elmagoelg...@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
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>>>>>>>>>>>>>>>>>>>>> To post to this group, send email to
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>>>>>>>>>>>>>>>>>>>>> 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
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>>>>>>>>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>>>>>>>> For more options, visit
>>>>>>>>>>>>>>>>>>>>> https://groups.google.com/d/optout.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> ____________________________________________________________
>>>>>>>>>>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> --
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>>>>>>>>>>>>>>>>>>> .
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>>>>>>>>>>>>>>>>>>>
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>>>>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout
>>>>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> ____________________________________________________________
>>>>>>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> --
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>>>>>>>>>>>>>> .
>>>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>>
>>>>>>>>>>>>> ____________________________________________________________
>>>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>>>>>>>>>>>>>
>>>>>>>>>>>> --
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>>>>>>>>>>>> .
>>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> --
>>>>>>>>>>>
>>>>>>>>>>> ____________________________________________________________
>>>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>>>>>>>>>>>
>>>>>>>>>> --
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>>>>>>>>>> .
>>>>>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>>
>>>>>>>>> ____________________________________________________________
>>>>>>>>> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>>>>>>>>>
>>>>>>>> --
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> To view this discussion on the web visit
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>> <https://groups.google.com/d/msgid/tesseract-ocr/CAGJ7VxHkuWyjWdum43qfFZc7x9M-jRJ4CGLK8VJpWmmVbr0%2B9w%40mail.gmail.com?utm_medium=email&utm_source=footer>
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>> For more options, visit https://groups.google.com/d/optout.
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>
> ____________________________________________________________
> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
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