Applying logic neural networks to hand-written character recognition tasks

Author

Tambouratzis, G.

Author_Institution

Inst. for Language & Speen Processing, Athens, Greece

fYear

1996

fDate

16-19 Nov. 1996

Firstpage

268

Lastpage

271

Abstract

This article discusses the implementation of a hand-written character recognition task using neural networks. Two logic neural networks-the WISARD (I. Aleksander and H. Morton, 1990) and the SOLNN (G. Tambouratzis and T.J. Stonham, 1993)-are compared on the basis of their classification accuracy. The results obtained are compared to these of other researchers, to objectively assess the success of the neural networks in classifying the dataset.

Keywords

character recognition; handwriting recognition; neural nets; pattern classification; self-organising feature maps; SOLNN; WISARD; classification accuracy; handwritten character recognition; logic neural networks; n-tuple statistical pattern recognition; self-organising logic neural network; Character recognition; Hamming distance; Logic circuits; Natural languages; Neural networks; Pattern recognition; Random access memory; Read-write memory; Retina; Speech processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1996., Proceedings Eighth IEEE International Conference on

ISSN

1082-3409

Print_ISBN

0-8186-7686-7

Type

conf

DOI

10.1109/TAI.1996.560461

Filename

560461

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2612637