Chris: thanks much for the awesome tip! My problem was exactly the same as yours. Training Tesseract is not the best way you would want to spend your days. So I followed this tip and saved myself a hell lot of heartburn.
On Monday, February 13, 2012 2:44:49 PM UTC+5:30, Chris wrote: > > I've given up on retraining tesseract. I can't get the same accuracy > as the default training data with the sample box data. > > But I solved my problem of app size by unpacking the training data, > deleting the bits I don't need and then packaging it back up. > > combine_tessdata -u eng.traineddata eng. > > delete the bits you don't need - in my case I don't need any of the > dawg files as I'm just recognising single chars > > then do: > > combine_tessdata eng. > > > > On Feb 12, 2:59 pm, Chris <[email protected]> wrote: > > I think you are right - I don't think the sample box data provided for > > download can be the same data that is used by google to create the > > trained data. > > > > On Feb 12, 12:42 pm, Zdenko Podobný <[email protected]> wrote: > > > > > > > > > > > > > > > > > Hi Chris, > > > > > I have the same experience - that leads me to conclusion it does not > > > make sense to train "common" fonts... > > > I think google use different process (more detailed; more/other > tools?) > > > comparing to information available on wiki... IMHO situation is > > > improving with each release, so I wait for additional information > > > regarding 3.02 training. > > > > > On other hand there is place for community to train "non-standard" > fonts > > > (e.g. in my case fraktur). I planned to write blog about my experience > > > when I helped to Slovak version of project Gutenberg, but there is > > > always something more urgent... ;-) > > > > > Zdenko > > > > > Dn(a 11.02.2012 14:47, Chris wrote / nap�sal(a): > > > > > > I also tried training with all the data. I seem to have the same > > > > problem with accuracy being much less than what you get with the > > > > default one. > > > > > > One thing that looks a bit off is my unicharset file contains lots > of > > > > NULLS and contents doesn't seem to match the documentation on doing > > > > training: > > > > > > 108 > > > > NULL 0 NULL 0 > > > > t 3 0,255,0,255 NULL 41 # t [74 ]a > > > > h 3 0,255,0,255 NULL 81 # h [68 ]a > > > > a 3 0,255,0,255 NULL 57 # a [61 ]a > > > > n 3 0,255,0,255 NULL 14 # n [6e ]a > > > > P 5 0,255,0,255 NULL 30 # P [50 ]A > > > > o 3 0,255,0,255 NULL 25 # o [6f ]a > > > > e 3 0,255,0,255 NULL 58 # e [65 ]a > > > > : 10 0,255,0,255 NULL 8 # : [3a ]p > > > > r 3 0,255,0,255 NULL 52 # r [72 ]a > > > > etc... > > > > > > Also when combining the files I get this output: > > > > > > Combining tessdata files > > > > TessdataManager combined tesseract data files. > > > > Offset for type 0 is -1 > > > > Offset for type 1 is 108 > > > > Offset for type 2 is -1 > > > > Offset for type 3 is 3961 > > > > Offset for type 4 is 701702 > > > > Offset for type 5 is 702267 > > > > Offset for type 6 is -1 > > > > Offset for type 7 is 716918 > > > > Offset for type 8 is -1 > > > > Offset for type 9 is 717216 > > > > Offset for type 10 is -1 > > > > Offset for type 11 is -1 > > > > Offset for type 12 is -1 > > > > > > So I obviously don't have all the necessary files. Would this effect > > > > accuracy when recognising single characters? > > > > > > On Feb 11, 10:17 am, Chris<[email protected]> wrote: > > > >> Hi All, > > > > > >> I'm using tesseract quite successfully in my code. I have a > > > >> preprocessing step that locate the characters I need to recognise > and > > > >> then I feed them into tesseract using the PSM_SINGLE_CHAR mode. > > > > > >> This works great with the default eng.traineddata > > > > > >> I'm also constraining the tessedit_char_whitelist to just have > numbers > > > >> and upper case letters as that is the only thing I have in my > > > >> character set. > > > > > >> I want to reduce the size of my app and the traineddata is by far > the > > > >> largest chunk of data at the moment. > > > > > >> What I've tried to do is retrain tesseract so that it only has the > > > >> characters I need in the training data. I've done this > successfully, > > > >> but when I use my newly created eng.traineddata the accuracy is > much > > > >> worse than if I use the default eng.traineddata. > > > > > >> Any ideas why this should be? I thought if anything that accuracy > > > >> would improve if I'd removed all the unnecessary characters from > the > > > >> data. > > > > > >> I'm doing my training by taking the box files and stripping out all > > > >> the characters I don't need and then running through the training > > > >> instructions. > > > > > >> I'm using tesseract3.01 > > > > > >> Any thoughts? > > > > > >> Cheers > > > >> Chris. -- -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. 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