Voronoi Tessellation for Effective and Efficient Handwritten Digit Classification

The aim of this paper is to explore the properties of a new zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Extensive experiments have been conducted on the MNIST dataset to investigate strengths and weakness of the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results with any type of features. Furthermore, the proposed zoning method, jointly with a suitable choice of features, allows a low complexity classifier to reach excellent performances both in terms of accuracy and speed.