caa a22 4500 467884226 CHVBK 20180406152729.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s10032-005-0005-6 doi (NATIONALLICENCE)springer-10.1007/s10032-005-0005-6 Genetic engineering of hierarchical fuzzy regional representations for handwritten character recognition [Elektronische Daten] [Christian Gagné, Marc Parizeau] This paper presents a genetic programming based approach for optimizing the feature extraction step of a handwritten character recognizer. This recognizer uses a simple multilayer perceptron as a classifier and operates on a hierarchical feature space of orientation, curvature, and center of mass primitives. The nodes of the hierarchy represent rectangular sub-regions of their parent node, the tree root corresponding to the character's bounding box. Within each sub-region, a variable number of fuzzy features are extracted. Genetic programming is used to simultaneously learn the best hierarchy and the best combination of fuzzy features. Moreover, the fuzzy features are not predetermined, they are inferred from the evolution process which runs a two-objective selection operator. The first objective maximizes the recognition rate, and the second minimizes the feature space size. Results on Unipen data show that, using this approach, robust representations could be obtained that out-performed comparable human designed hierarchical fuzzy regional representations. Springer-Verlag, 2005 On-line character recognition nationallicence Handwriting nationallicence Evolutionary computations nationallicence Fuzzy logic nationallicence Unipen dataset nationallicence Gagné Christian Laboratoire de Vision et Systèmes Numériques (LVSN), Département de Génie Électrique et de Génie Informatique, Université Laval, G1K 7P4, Québec (Québec), Canada aut Parizeau Marc Laboratoire de Vision et Systèmes Numériques (LVSN), Département de Génie Électrique et de Génie Informatique, Université Laval, G1K 7P4, Québec (Québec), Canada aut International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/4(2006-09-01), 223-231 1433-2833 8:4<223 2006 8 10032 https://doi.org/10.1007/s10032-005-0005-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-005-0005-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gagné Christian Laboratoire de Vision et Systèmes Numériques (LVSN), Département de Génie Électrique et de Génie Informatique, Université Laval, G1K 7P4, Québec (Québec), Canada aut NATIONALLICENCE 700 1- Parizeau Marc Laboratoire de Vision et Systèmes Numériques (LVSN), Département de Génie Électrique et de Génie Informatique, Université Laval, G1K 7P4, Québec (Québec), Canada aut NATIONALLICENCE 773 0- International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/4(2006-09-01), 223-231 1433-2833 8:4<223 2006 8 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer