The file is probably there as script/Latin.traineddata You can copy to wherever you are looking for the best traineddata files.
On Fri, Apr 10, 2020, 16:59 O CR <[email protected]> wrote: > Which language do I have to use? Because Latin isn't supported. > ./tesstrain.sh --fonts_dir "/usr/share/fonts" *--lang Latin* > --linedata_only --noextract_font_properties --langdata_dir ./langdata > --tessdata_dir ./tessdata --output_dir ./output > > Op woensdag 8 april 2020 18:27:15 UTC+2 schreef shree: >> >> I suggest you fine-tune Latin.traineddata using text of the kind you >> expect. It will have a smaller unicharset and when you convert to fast >> integer model, it should be smaller in size. >> >> On Wed, Apr 8, 2020, 20:39 O CR <[email protected]> wrote: >> >>> Hi all, >>> >>> I try to read names on images with tesseract LSTM. Names like: >>> >>> Śerena Kovitch >>> >>> ŁAGUNA EVREIST >>> >>> Äna Optici >>> >>> Orğu Moninck >>> >>> >>> (I don't have to recognize words) >>> >>> >>> Latin.traineddata (fast integer) is doing well with the diacritics, but >>> there are a lot of characters I don't need like numbers, %, ﹕ ,﹖ ,﹗,﹙ >>> ,﹚ ,﹛ ,﹜ ,﹝ ,﹞ ,﹟ ,﹠ ,﹡ ,﹢ ,﹣ ,﹤,﹥,﹦ ,﹨ ,﹩ ﹪ ,﹫,and much more. And so >>> Latin.traineddata is too slow. >>> >>> So I thought I take eng.traineddata (best float for LSTM) and I train it >>> for the diacritics. But there are almost 400 diacritics. So I don't know if >>> fine-tuning for such amount of characters is a good idea? >>> >>> However I tried it but the quality is very poor. >>> >>> I trained with eng.training_text (a English text of 72 lines) and I >>> added all the diacritics several times. The char error rate during lstmeval >>> is around 0.1. I did a test with 80 documents, and I read 30 names correct. >>> (on each document there is one name). (time is similar to Latin.traineddata) >>> >>> >>> What can I do to get a model that is as good as Latin.traineddata on >>> diacritics but is much faster in ocr reading? >>> >>> >>> Thank you. >>> >>> -- >>> 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 [email protected]. >>> To view this discussion on the web visit >>> https://groups.google.com/d/msgid/tesseract-ocr/b9ddf333-1229-45d3-9a02-809973294a47%40googlegroups.com >>> <https://groups.google.com/d/msgid/tesseract-ocr/b9ddf333-1229-45d3-9a02-809973294a47%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >> -- > 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 [email protected]. > To view this discussion on the web visit > https://groups.google.com/d/msgid/tesseract-ocr/d692a36f-81c4-4226-94d6-15ec8238673b%40googlegroups.com > <https://groups.google.com/d/msgid/tesseract-ocr/d692a36f-81c4-4226-94d6-15ec8238673b%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- 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 [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/tesseract-ocr/CAG2NduVsef21aUHi90Y0S3c__zzUHqdtGhMyfY46UXhJNUfO9Q%40mail.gmail.com.
