HI, Well, I read the description of ScrollView ( https://github.com/tesseract-ocr/tesseract/wiki/ViewerDebugging) and it says:
To show the characters, deselect DISPLAY/Bounding Boxes, select DISPLAY/Polygonal Approx and then select OTHER/Uniform display. It basically works. But for some reason, it doesn't work on my e13b image and ends up with a blue screen. Anyway, it shows each box separately when a character is consist of multiple boxes. I'd like to show the box for the whole character. ScrollView doesn't do it, at least, yet. I'll do it on my own. ElMagoElGato 2019年7月24日水曜日 14時10分46秒 UTC+9 ElGato ElMago: > > Hi, > > > I got this result from hocr. This is where one of the phantom characters > comes from. > > <span class='ocrx_cinfo' title='x_bboxes 1259 902 1262 933; x_conf > 98.864532'><</span> > <span class='ocrx_cinfo' title='x_bboxes 1259 904 1281 933; x_conf > 99.018097'>;</span> > > > The firs character is the phantom. It starts with the second character > that exists on x axis. The first character only has 3 points width. I > attach ScrollView screen shots that visualize this. > > [image: 2019-07-24-132643_854x707_scrot.png][image: > 2019-07-24-132800_854x707_scrot.png] > > > There seem to be some more cases to cause phantom characters. I'll look > them in. But I have a trivial question now. I made ScrollView show these > displays by accidentally clicking Display->Blamer menu. There is Bounding > Boxes menu below but it ends up showing a blue screen though it briefly > shows boxes on the way. Can I use this menu at all? It'll be very useful. > > [image: 2019-07-24-140739_854x707_scrot.png] > > > 2019年7月23日火曜日 17時10分36秒 UTC+9 ElGato ElMago: >> >> 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> 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 <a href=" >>>>>>>>>>>>>>>>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/2c6fe865-911d-41f3-9926-cbfb56db794f%40googleg >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. 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