A modular neural system for handwritten alphabet recognition using invariant moment-based features

Author

Benromdhane, Saida ; Salam, F.M.A.

Author_Institution

Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA

fYear

1992

fDate

17-19 Sep 1992

Firstpage

357

Lastpage

360

Abstract

A recognition system for identifying handwritten alphabets using a specific artificial neural network structure is described. Layers of feedforward networks preprocess the alphabets by extracting features that enhance the characters´ dissimilarity. The features approximate the moments and moment invariants which are invariant to translation, rotation, and scaling of the processed alphabets. The collective tasks of the feedforward network modules integrate feature extraction preprocessing with classification

Keywords

character recognition; feature extraction; feedforward neural nets; artificial neural network structure; character recognition; classification; feature extraction; feedforward networks; handwritten alphabet recognition; invariant moment-based features; modular neural system; moment invariants; rotation; scaling; translation; Artificial neural networks; Data mining; Feature extraction; Feedforward neural networks; Handwriting recognition; Logistics; Neural networks; Neurons; Pixel; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems Engineering, 1992., IEEE International Conference on

Conference_Location

Kobe

Print_ISBN

0-7803-0734-8

Type

conf

DOI

10.1109/ICSYSE.1992.236883

Filename

236883