Image recognition via deformable templates

High quality handwritten numeral recognition is currently seen as an essential capability in the area of office automation. Statistical algorithms applied to this task can have a very significant impact because they offer the possibility of generalizing the information contained in a training set to new examples, without requiring an extensive programming effort tailored to specific hand-written styles. In this thesis, we apply the deformable probabilistic template approach to a problem of recognizing handwritten digits. Unlike previous published works in this area, the template is fed directly with images, rather than feature vectors, thus demonstrating the ability of Bayesian models to deal with large amounts of global and high-level information.