Experiments with various neural architectures for handwritten character recognition

Author

Koutsougeras, Cris ; Jameel, Akhtar

Author_Institution

Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA

Volume

1

fYear

1994

fDate

3-5 Aug 1994

Firstpage

573

Abstract

In this research we address the problem of recognition of isolated handwritten characters. Handwritten character recognition has been a topic of research for a long period of time. It has been argued that this problem is difficult to model using classical modeling techniques, and that neural networks offer a new perspective to approaching this problem. This paper outlines the experimental evidence we have compiled while investigating possible approaches to handwritten character recognition. It is the hypothesis of our approach that handwritten character recognition is a pattern recognition problem and that there exists a set of unique features in the data which can be used for classification

Keywords

character recognition; feature extraction; neural net architecture; pattern classification; handwritten character recognition; isolated handwritten characters; neural architectures; pattern classification; pattern recognition problem; unique features; Automation; Character recognition; Computer architecture; Computer science; Feedforward neural networks; Feedforward systems; Handwriting recognition; Neural networks; Pattern recognition; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on

Conference_Location

Lafayette, LA

Print_ISBN

0-7803-2428-5

Type

conf

DOI

10.1109/MWSCAS.1994.519303

Filename

519303