Text-independent off-line writer recognition using neural networks

Abstract

In this paper we present a text-independent offline writer recognition system based on multilayer perceptrons (MLPs). The system can be used for both identification and verification purposes. It was tested on a population of 20 writers with non-correlated training and test specimens. The mean error for identification was 3.5% while error rates as low as 0.5% were achieved on specimens with more than 25 characters. For verification the mean error was 1.2% (2.22% false rejection, 0.18% false acceptance) considering a minimum of 15 characters per test specimen. These error rates are comparable to those achieved by classical methods while the response of the system is substantially faster.