Improvements on handwritten digit recognition by cooperation of modular neural networks

In this paper modular neural networks are used to improve handwritten digit recognition. To evaluate the performance of modular networks, a comparison is made with a global neural network, on the same database. Two basic kind of modular networks are considered: 1) seven expert modular networks in which five of them are provided for digits 0, 1, 2, 5, 6, 7 and the rest for the pair of digits 3-8 and 4-9 respectively; and 2) a modular neural network with an expert module for each feature extracted from the handwritten digit image. The cooperation is among modules extracting slope and radial projection from each digit. Two type of cooperation among modular networks are considered: neural network and weighted combination of the modules outputs. The models were trained and tested on a different set of digits. The results show that by using modular network for features, it is possible to improve classification performance on handwritten digits, from 91.0% in the case of global networks to 93.5% of modular networks.