[liabru]

This is great. I particularly like that they also automatically generated dirty versions for their training set, because that's exactly what I ended up doing for my dissertation project (a computer vision system [1] that automatically referees Scrabble boards). I also used dictionary analysis and the classifier's own confusion matrix to boost its accuracy.

If you're also interested in real time OCR like this, I did a write up [2] of the approach that worked well for my project. It only needed to recognize Scrabble fonts, but it could be extended to more fonts by using more training examples.

[1] http://brm.io/kwyjibo/

[2] http://brm.io/real-time-ocr/

[joe_the_user]

It seems your dissertation paper is behind something password protected [1]. It would be nice to see that too.

Can't get [1]https://www.dcs.shef.ac.uk/intranet/teaching/campus/projects...

[liabru]

Hmm looks like they have, well here's another link to it: https://dl.dropboxusercontent.com/u/1672291/scrabble-referee...

[JabavuAdams]

Glad there's prior art on that. I had a small project where I iterated all the fonts on the system and used them to generate glyph training images. The next step was to dirty them up, but I never continued the project.

More generally, I really like the idea of generating controlled synthetic images and then messing them up for regularization.

[megalodon]

Funny, just read an article today proposing the same feature detection algorithm (the one you called 'grid merge'). Have you tried applying these techniques on scanned/photographed documents?

[liabru]

Could you link to it please?

I've not tried it on anything else, but I remember thinking that it has a lot of potential uses. Also I only used it on gray-scale features, but I'm sure it could make use of full RGB too. I'll have to try it some time!

[zem]

excellent project. as a scrabble player, i'm very interested - it would be a great way to run a blitz tournament, for instance.