DocumentCode
396676
Title
Permutative coding technique for handwritten digit recognition system
Author
Kussul, E. ; Baidyk, T.
Author_Institution
Center of Appl. Sci. & Technol. Dev., UNAM, Mexico City, Mexico
Volume
3
fYear
2003
fDate
20-24 July 2003
Firstpage
2163
Abstract
The new neural classifier for the handwritten digit recognition is proposed. The classifier is based on the Permutative Coding technique. This coding technique is derived from the associative-projective neural networks developed in the 80th-90th. The classifier performance was tested on the MNIST database. The error rate of 0.54% was obtained.
Keywords
encoding; feature extraction; handwritten character recognition; neural nets; MNIST database; associative-projective neural networks; encoding; feature extraction; handwritten digit recognition system; neural classifier; permutative coding technique; Brightness; Convolution; Error analysis; Feature extraction; Handwriting recognition; Image databases; Neural networks; Shape; Spatial databases; System testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223743
Filename
1223743
Link To Document