Even though version 0.5 was released just a couple of weeks ago,
the next release, 0.6, is approaching.
As I did with the previous release,
I'll blog about the new features it will include
as they are more or less ready.
The first feature I'd like to talk about
is a new subsystem for recognizing handwritten digits,
which replaces the one that I wrote in the early days of Eyegrade.
a former student of mine
at Universidad Carlos III de Madrid,
volunteered to improve the previous system with a
he used a support vector machine (SVM).
The machine is trained by showing it lots of samples of each digit,
and telling it which digit each sample represents.
you can show an image of a digit to it,
and it will answer with the digit it considers
the best match for the image.
Rodrigo tried with several strategies
before finally integrating one into Eyegrade.
Our first tests in real scenarios show that,
under conditions in which the former system failed
approximately once for every 6 student identifications,
the new system fails approximately once for every 20.
I believe it is a big step forward.
Many thanks to Rodrigo for his great contribution,
and to Roberto for sending us tons of samples
for training and testing the new system.
Removing the old digit recognition system
provides a second benefit:
the tre library is no longer needed.
The fact that it wasn't a pure Python library,
and that it doesn't seem to be widely used from Python,
was a headache for deploying the Windows version,
and complicated the installation procedure in Linux a little bit.
If everything goes as planned,
you can expect a greatly simplified installation procedure,
especially for Windows users.
More on this topic on my next post.