A digitized handwritten numeral can be represented as a binary or greyscale image. An important pattern recognition task that has received much attention lately is to automatically determine the digit, given the image.
While many different techniques have been pushed very hard to solve this task, the most successful and intuitively appropriate is due to Simard, Le Cun and Denker (1993). Their approach combined nearest-neighbor classification with a subject-specific invariant metric that allows for small rotations, translations and other natural transformations. We report on Simard's classifier and compare it to other approaches. One important negative aspect of near-neighbor classification is that all the work gets done at lookup time, and with around 10,000 training images in high dimensions this can be exorbitant.
In this paper we develop rich models for representing large subsets of the prototypes. One example is a low-dimensional hyperplane defined by a point and a set of basis or tangent vectors. The components of these models are learned from the training set, chosen to minimize the average tangent distance from a subset of the training images--as such they are similar in flavor to the singular value decomposition (SVD), which finds closest hyperplanes in Euclidean distance. These models are either used singly per class or used as basic building blocks in conjunction with the $K$-means clustering algorithm.
"Metrics and models for handwritten character recognition." Statist. Sci. 13 (1) 54 - 65, February 1998. https://doi.org/10.1214/ss/1028905973