An Application of Data Compression Models to Handwritten Digit Classification

Abstract

In this paper, we address handwritten digit classification as a special problem of data compression modeling. The creation of the models—usually known as training—is just a process of counting. Moreover, the model associated to each class can be trained independently of all the other class models. Also, they can be updated later with new examples, even if the old ones are not available anymore. Under this framework, we show that it is possible to attain a classification accuracy consistently above 99.3% on the MNIST dataset, using classifiers trained in less than one hour on a common laptop.