nerative Models for andwritten Digit Recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B- splines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. 1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. 2) During the process of explaining the image, generative models can perform recognition driven segmentation. 3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. 4) Unlike many other recognition schemes, it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques. Index Terms-Deformable model, elastic net, optical character recognition, generative model, probabilistic model, mixture model