For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
The performance of a novel multiple expert decision combination strategy has been compared with other multiple expert decision combination methods reported in the literature. The concept of decision combination has been generalised in two different categories and it has been demonstrated how these different categories perform with respect to each other under optimised conditions. The paper presents the performance of this particular network, which is a Type-II network and compares it with other Type-I decision combination strategies previously reported in literature. These methods include aggregation method, choice selection and ranking method. In all the cases, the chosen database was the NIST database, which is recognised to be the standard database for handwritten characters. It has been found that this particular Type-II configuration is able to outperform all these Type-I combination strategies. The performance enhancement on a subset of the NIST database having a thousand character samples for each class has been found to be around 1.2% with respect to the best recognition performance obtained from either of the Type-I decision combination strategies investigated.