An artificial neural network approach to handwriting recognition

Author

Goh, W.L. ; Mital, D.P. ; Babri, H.A.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore

Volume

1

fYear

1997

fDate

27-23 May 1997

Firstpage

132

Abstract

This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second

Keywords

backpropagation; feature extraction; handwriting recognition; image classification; neural nets; optical character recognition; performance evaluation; artificial neural network; classification; error backpropagation; feature extraction; handwriting recognition; handwritten character recognition; memory requirements; recognition accuracy; response speed; time; Artificial neural networks; Character recognition; Feature extraction; Handwriting recognition; Intelligent networks; Intelligent systems; Neural networks; Pattern recognition; Rendering (computer graphics); Sorting;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3755-7

Type

conf

DOI

10.1109/KES.1997.616872

Filename

616872