Title :
Extracting meaningful handwriting features with fuzzy aggregation method
Author :
Malaviya, Ashutosh ; Peters, Liliane
Author_Institution :
German Nat. Res. Center for Comput. Sci., St. Augustin, Germany
Abstract :
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
Keywords :
computational linguistics; feature extraction; formal languages; fuzzy set theory; handwriting recognition; inference mechanisms; knowledge based systems; uncertainty handling; character recognition; fuzzy aggregation method; fuzzy feature extraction; fuzzy rule base; handwriting data; linguistic rules; meaningful handwriting feature extraction; multistage feature aggregation scheme; novel fuzzy methodology; prototype class; recognition accuracy; Character recognition; Data mining; Feature extraction; Fuzzy sets; Handwriting recognition; Humans; Pattern recognition; Prototypes; Shape; Writing;
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
DOI :
10.1109/ICDAR.1995.602032
