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.
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