Title :
Handwritten numerical recognition with neural networks and information fusion
Author :
Cao, J. ; Shridhar, M. ; Ahmadi, M.
Author_Institution :
Dept. of Electr. Eng., Windsor Univ., Ont., Canada
Abstract :
Recently, in the area of character recognition, the concept of combining multiple classifiers has been proposed as a promising direction for the development of robust recognition systems. In this paper, an evidence fusion technique, based on the notion of fuzzy integral is utilized to obtain a reliable, high accuracy handwritten character recognition system. Experiments with a large real world data set reveal the robustness of this system
Keywords :
character recognition; fuzzy logic; neural nets; pattern classification; character recognition system; evidence fusion technique; fuzzy integral; handwritten numerical recognition; information fusion; multiple classifiers; neural networks; real world data set; robust recognition systems; Density functional theory; Density measurement; Extraterrestrial measurements; Fuzzy neural networks; Fuzzy sets; Handwriting recognition; Integral equations; Neural networks; Q measurement; Testing;
Conference_Titel :
Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on
Conference_Location :
Lafayette, LA
Print_ISBN :
0-7803-2428-5
DOI :
10.1109/MWSCAS.1994.519302
