Extracting meaningful handwriting features with fuzzy aggregation method
Ashutosh Malaviya, Liliane Peters · 2002
Recognition methods use different features to assign a pattern to a prototype class. The recognition accuracy strongly depends on the selected features. We present a novel fuzzy methodology to extract appropriate fuzzy features from the handwriting data. From these meaningful features a set of linguistic rules are derived which in turn constitute a fuzzy rule base for character recognition. The fuzzy features are confined to their meaningfulness with the help of a multistage feature aggregation scheme.