DocumentCode
290276
Title
Transformation of optimized prototypes for handwritten digit recognition
Author
Yan, Hong
Author_Institution
Dept. of Electr. Eng., Sydney Univ., NSW, Australia
Volume
ii
fYear
1994
fDate
19-22 Apr 1994
Abstract
Proposes a method for handwritten digit recognition using optimized prototypes generated through learning and transformation. In this method a set of prototypes are obtained from training samples and mapped to a multi-layer neural network for optimization to improve their classification power. The new prototypes are then transformed geometrically to produce a larger set of prototypes for recognition of testing samples. The method has been verified to work well in experimental studies
Keywords
image classification; learning (artificial intelligence); multilayer perceptrons; optical character recognition; optimisation; classification power; handwritten digit recognition; learning; multilayer neural network; optimized prototypes; training; transformation; Deformable models; Handwriting recognition; Multi-layer neural network; Neural networks; Optimization methods; Prototypes; Robustness; Testing; Training data; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location
Adelaide, SA
ISSN
1520-6149
Print_ISBN
0-7803-1775-0
Type
conf
DOI
10.1109/ICASSP.1994.389578
Filename
389578
Link To Document