hernik/tessdata_fast
1
0
Fork 0
mirror of https://github.com/tesseract-ocr/tessdata_fast.git synced 2024-11-21 21:51:05 +01:00
Code Issues Projects Releases Wiki Activity

Merge pull request #6 from Shreeshrii/patch-1

Browse source 
Update README.md for script level traineddata file info
This commit is contained in:
zdenop 2018-02-21 08:43:09 +01:00 committed by GitHub
commit 8203e55ebb
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 26 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

26
README.md
View file

@ -2,6 +2,32 @@
This repository contains fast integer versions of trained models for the
[Tesseract Open Source OCR Engine](https://github.com/tesseract-ocr/tesseract).
Most users will want to use these traineddata files and this is what is planned to be shipped as part of Linux distributions. Fine tuning/incremental training will **NOT** be possible from these `fast` models, as they are 8-bit integer. It will be possible to convert a tuned `best` to integer to make it faster, but some of the speed in `fast` will be from the smaller model.
When using the models in this repository, only the new LSTM-based OCR engine is supported. The legacy `tesseract` engine is not supported with these files, so Tesseract's oem modes '0' and '2' won't work with them.
Initial capitals indicate the one model for all languages in that script.
**Latin** is all latin-based languages,
except vie, which has its own **Vietnamese**.
**Devanagari** is hin+san+mar+nep+eng
**Fraktur** is basically a combination of all the latin-based languages that have an 'old' variant.
Most of the script models include English training data as well as the script, but not for **Cyrillic**, as that would have a major ambiguity problem.
For Latin-based languages, the existing model data provided has been trained on about 400000 textlines spanning about 4500 fonts. For other scripts, not so many fonts are available, but they have still been trained on a similar number of textlines.
For Latin, I have ~4500 fonts to train with. For Devanagari ~50, and for Kannada 15. With a theory that poor accuracy on test data and over-fitting on training data was caused by the lack of fonts, I tried mixing the training data with English, thinking that English is often mixed in anyway, and some of the font diversity might generalize to the other script. The overall effect was slightly positive, so I left it that way.
'jpn' contains whatever appears on the www that is labelled as the language, trained only with fonts that can render Japanese. As with most of the other Script traineddatas, **Japanese** contains all the languages that use that script (in this case just the one) PLUS English.The resulting model is trained with a mix of both training sets, with the expectation that some of the generalization to 4500 English training fonts will also apply to the other script that has a lot less.
'jpn_vert' is trained on text rendered vertically (but the image is rotated so the long edge is still horizontal).
'jpn' loads 'jpn_vert' as a secondary language so it can try it in case the text is rendered vertically. This seems to work most of the time as a reasonable solution.
See the [Tesseract wiki](https://github.com/tesseract-ocr/tesseract/wiki/Data-Files)
for additional information.