This paper presents experiments with genetically engineered feature sets for recognition of online handwritten characters. These representations stem from a nondescript decomposition of the character frame into a set of rectangular regions, possibly overlapping each represented by a vector of 7 fuzzy variables. Efficient new feature sets are automatically discovered using genetic programming techniques. Recognition experiments conducted on isolated digits of the Unipen database yield improvements of more than 3% over a previously, manually designed representation where region positions and sizes were fixed.
Genetic engineering of handwriting representations
2002-01-01
325754 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Genetical Engineering of Handwriting Representations
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