I am working on a project that requires classification of characters and symbols (basically OCR that needs to handle single ASCII characters and symbols such as music notation). I am working with vector graphics (Paths and Glyphs in WPF) so the images can be of any resolution and rotation will be negligable. It will need to classify (and probably learn from) fonts and paths not in a training set. Performance is important, though high accuracy takes priority.
I have looked at some examples of image detection using Emgu CV (a .Net wrapper of OpenCV). However examples and tutorials I find seem to deal specifically with image detection and not classification. I don’t need to find instances of an image within a larger image, just determine the kind of symbol in an image.
There seems to be a wide range of methods to choose from which might work and I’m not sure where to start. Any advice or useful links would be greatly appreciated.
You should probably look at the paper: Gradient-Based Learning Applied to Document Recognition, although that refers to handwritten letters and digits. You should also read about Shape Context by Belongie and Malik. They keyword you should be looking for is digit/character/shape recognition (not detection, not classification).