A Novel Technique for Handwritten Digit Classification using Genetic Clustering

The aim of this paper is to introduce a novel technique for handwritten digit recognition based on genetic clustering. Cluster design is proposed as a two-step process. The first step is focused on generating cluster solutions, while the second one involves the construction of the best cluster solution starting from a set of suitable candidates. An approach for achieving these goals is presented. Clustering is considered as an optimization problem in which the objective function to be minimized is the cost function associated to the classification. A genetic algorithm is used to determine the best cluster centers to reduce classification time, without greatly affecting the accuracy. The classification task is performed by k-nearest neighbor classifier. It has also been developed a new feature and a distance measure based on the Sokal-Michener dissimilarity measure to describe and compare handwritten numerals. This technique has been evaluated through experimental testing on MNIST dataset and its effectiveness has been proved.