Abstract
This paper presents experiments with genetically
engineered feature sets for recognition of on-line
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 3percent over
a previously manually designed representation where
region positions and sizes were fixed.
- algorithms,
- base
- character
- classification,
- database,
- decomposition,
- extraction,
- feature
- floating
- frame
- fuzzy
- fuzzy-regional
- genetic
- handwriting
- handwritten
- operators,
- pattern
- programming,
- recognition,
- region
- regions,
- representation,
- representations,
- set
- sets,
- theory,
- unipen
Users
Please
log in to take part in the discussion (add own reviews or comments).