I'm using the word confusable to represent similar looking glyphs in text.
I'm building an optical character recognition tool with the primary goal of experimenting with machine learning – especially neural nets, which I already have some knowledge about. There is one issue that I just can't get my head around: Representing confusables.
As an example
I (capital I) and
l (small L) looks the same in sans-serif fonts. If you extend it to the complete Unicode range, there are a lot of glyphs that look more or less the same. If I'm using supervised learning naïvely, I'm afraid that the training algorithm won't be effective because of these confusables.
Maybe I need unsupervised learning. I could use pretraining on RBMs or stacked auto-encoders, but I'm not sure you can assume that all confusables are close in the output space. They might be. The task still remains to detect these areas and make a good representation.
I'm basically looking for a good model to cluster confusables, which I can use to create an improved supervised learning model. I expect to do some post-analyses on the output of the classifier in order to resolve each confusable to a real Unicode character.