# Good machine learning models for confusable categories

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.

• I totally agree that it can be solved on a higher level. I want to get the best data for this level. As an example, my classifier could output: l (48%), I (47%), 1 (5%). And the higher level would have a better chance at getting it right. – Bjarke Walling Jul 23 '14 at 18:54