Title :
Multiple classifier fusion for handwritten word recognition
Author :
Gader, Paul D. ; Mohamed, Magdi A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
Abstract :
A method for fusing recognition results from multiple handwritten word recognition algorithms is presented. The fusion algorithm relies on a novel application of the Choquet fuzzy integral. The novel application uses data dependent densities for the fuzzy measure. Three handwritten word recognition algorithms are described. A recognition rate of 88% is achieved on the bd city word test set from standard SUNY CDROM database. This rate is higher than those achieved using Borda counts, weighted counts, and fuzzy integrals with data-independent densities
Keywords :
fuzzy set theory; optical character recognition; Choquet fuzzy integral; bd city word test set; data-dependent densities; fuzzy measure; handwritten word recognition; multiple classifier fusion; standard SUNY CDROM database; Application software; Cities and towns; Classification algorithms; Databases; Density measurement; Fuses; Handwriting recognition; Pattern recognition; Shape; Testing;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538129
