If you can provide another 40-50 lines of training data (text file) I will rerun the training
On Mon, 8 Apr 2019, 22:11 Jankees Korstanje, <seek...@gmail.com> wrote: > Hi Shree, > > We have tried your traineddata file for MRZ and noticed that it does not > detect the character X. > > Looking at > https://github.com/Shreeshrii/tessdata_ocrb/blob/master/eng.MRZ.training_text > > We see that there are no X in there. > > In addition it might be good to add a couple of lines that are specific > for IDs (starting with I) note they are all fake > > IDESPANH186495123456789X<<<<<< > IXESPE002561410<0233181G<<<<< > I<NLDIS2KX87214<<<<<<<<<<<<<<< > > > > > > > > On Wednesday, 5 September 2018 18:03:41 UTC+2, shree wrote: >> >> See https://github.com/Shreeshrii/tessdata_ocrb >> for the files and traineddata. >> >> >> On Wed, Sep 5, 2018 at 8:51 PM, Shree Devi Kumar <shree...@gmail.com> >> wrote: >> >>> I think finetune will be a better option than training from scratch. >>> >>> Using a small training/test text - 40 lines, I get >>> >>> --------------------------------- >>> >>> + lstmeval --verbosity 0 --model /home/ubuntu/ >>> *tessdata_best/script/Latin.traineddata* --eval_listfile >>> /home/ubuntu/tesstutorial/ocrb/eng.training_files.txt >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR-B_10_BT.exp0.lstmf >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR_B_MT.exp0.lstmf >>> Warning: LSTMTrainer deserialized an LSTMRecognizer! >>> At iteration 0, stage 0, *Eval Char error rate=0.73106061*, *Word error >>> rate=13.75* >>> >>> --------------------------------- >>> >>> + lstmeval --verbosity 0 --model /home/ubuntu/ >>> *tessdata_best/eng.traineddata* --eval_listfile >>> /home/ubuntu/tesstutorial/ocrb/eng.training_files.txt >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR-B_10_BT.exp0.lstmf >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR_B_MT.exp0.lstmf >>> Warning: LSTMTrainer deserialized an LSTMRecognizer! >>> At iteration 0, stage 0, *Eval Char error rate=47.444889, Word error >>> rate=92.5* >>> >>> >>> * --------------------------------- * >>> >>> *At iteration 16/410/410, Mean rms=0.236%, delta=0.131%, char >>> train=0.448%, word train=3.659%, skip ratio=0%, New best char error = >>> 0.448 wrote checkpoint.* >>> >>> *Finished! Error rate = 0.448* >>> >>> >>> * --------------------------------- * >>> >>> >>> + lstmeval --model >>> /home/ubuntu/tesstutorial/ocrb_from_full/*ocrb_plus_checkpoint >>> *--traineddata /home/ubuntu/tesstutorial/ocrb/eng/eng.traineddata >>> --eval_listfile /home/ubuntu/tesstutorial/ocrb/eng.training_files.txt >>> /home/ubuntu/tesstutorial/ocrb_from_full/ocrb_plus_checkpoint is not a >>> recognition model, trying training checkpoint... >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR-B_10_BT.exp0.lstmf >>> Loaded 40/40 pages (1-40) of document >>> /home/ubuntu/tesstutorial/ocrb/eng.OCR_B_MT.exp0.lstmf >>> At iteration 0, stage 0, *Eval Char error rate=0, Word error rate=0* >>> >>> --------------------------------- >>> >>> On Wed, Sep 5, 2018 at 1:55 PM, <kaminski...@gmail.com> wrote: >>> >>>> Hi, >>>> >>>> (I might butcher English grammar- you have been warned!) >>>> >>>> For some time I'm trying to teach tesseract to read MRZ >>>> codes.Unfortunately it's not going very well. I'm using the latest version >>>> of tesseract (4.0) soI'mm trying to train it by lstm method. I've >>>> managed to pull it off and got some custom traineddata samples but >>>> effects of using them are... let's say slightly unsatisfying. In the matter >>>> of fact they are not even remotely close to eng traineddata. I know >>>> that there was mrz traineddata in the previous version of tesseract. >>>> >>>> I'm out of ideas how to improve accuracy, so I'll need your help guys. >>>> >>>> At first I thought I could use images, .txt files containing already >>>> read data and font data to somehow make box files (basically you have >>>> image and .txt containing everything read from the image). I was >>>> disappointed when I realized that without manual correction of boxes >>>> tesseract won't know how to apply them correctly. Of course I need >>>> automated method do apply boxes (I can't use any GUI or something). >>>> >>>> At the moment I'm only using .txt files and these are steps I'm doing >>>> (it's also good to mention that I'm trying to make it from scratch): >>>> -Using .txt and font (OcrB) to create .tiff and box files using >>>> text2image method >>>> -Creating unicharset from all box files >>>> -(it's optional but for the sake of it) I'm applyingunicharsetproperties >>>> >>>> -Getting trainneddata from unicharset, langdata and using custom >>>> language as parameter >>>> -Creating lstmf file by tesseract some .tiff output lstm.train >>>> -Creating list of files to train >>>> -Running lstm training with net spec [1,36,0,1 Ct3,3,16 Mp3,3 Lfys48 >>>> Lfx96 Lrx96 Lfx256 O1c111] and learning rate 20e-4 >>>> -At the end I'm using last checkpoint to create traineddata for usage. >>>> Currently initial .txt files are randomly generated by me in program >>>> in form of mrz code (samples included). I also tried to generate files >>>> in form of mixed alphabet to get signs variety. I was using about 1000 >>>> samples to train it and it doesn't differ from using 100 samples. >>>> >>>> Also, I disabled dictionary in the OCR process to prevent tesseract >>>> from treating whole MRZ code as a word. >>>> >>>> I might not understand some things despite reading a lot about this >>>> topic, but I'm pretty sure that I'm doing training process correctly. Do >>>> you have any tips how to improve training process? Consider pointing out >>>> even dumbest things I could forget about. >>>> >>>> -- >>>> 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/b3b86804-5d86-4fac-a780-88a2ef4f2ba2%40googlegroups.com >>>> <https://groups.google.com/d/msgid/tesseract-ocr/b3b86804-5d86-4fac-a780-88a2ef4f2ba2%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 >>> >> >> >> >> -- >> >> ____________________________________________________________ >> 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 tesseract-ocr+unsubscr...@googlegroups.com. > To post to this group, send email to tesseract-ocr@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/a8ddadfc-ac03-4169-8de3-68da65910ba6%40googlegroups.com > <https://groups.google.com/d/msgid/tesseract-ocr/a8ddadfc-ac03-4169-8de3-68da65910ba6%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. 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