Tesseract 4 has got a new long short-term memory neural networking based
OCR engine which really helps a lot in terms of accuracy and our VM
tests.
I ran the new version across a bunch of different screenshots and
comparing the results to the 3.x branch and it really makes a big
difference, especially with various font rendering settings.
The only downside of this is that version 4 hasn't been released yet and
is in alpha state right now, but it will eventually get there and the
only solutions that came into my mind sticking to version 3 were really
sub-par:
* Use several passes with different color negation on the screenshots.
* Train Tesseract 3 specifically for screenshots. This is sub-par
because we'd need to do it for Tesseract 4 from scratch again.
* Change the test systems so that it specifically uses *only* OCR an
font when displaying. I've actually tried this but this also isn't
accurate enough with our default font rendering setup.
* Turn off special font rendering settings for our tests. In
conjunction with changing to an OCR font this might work but it won't
catch all the cases, because applications might use their own font
rendering.
Given that version 4 is faster[1] when it comes to OCR detection and also
the points just mentioned I think even using the alpha version just for
tests isn't going to hurt anybody.
[1]: https://github.com/tesseract-ocr/tesseract/wiki/4.0-Accuracy-and-Performance
Signed-off-by: aszlig <aszlig@redmoonstudios.org>
