Text-independent off-line writer recognition using neural networks

A system of writer recognition using neural networks is described in this paper. The system is text independent and can be used for both identification and verification purposes. It consists of 20 multi-layer perceptrons as many, as the population of writers of the test. The letters used for training and testing were part of the Greek alphabet and were non-correlated. The system was tested on a total number of 5000 letters coming from the 20 writers. Error rates as low as 0.5% were achieved on test sets with more than 30 letters per set, in identification testing. In the verification testing the mean error was 1.2% on test sets with more than 15 letters per set. The response delay of the system was negligible (0.4 seconds on a conventional PC)