A feature extraction technique for online handwriting recognition

Author

Verma, Brijesh ; Lu, Jenny ; Ghosh, Moumita ; Ghosh, Ranadhir

Author_Institution

Fac. of Inf. & Commun., Central Queensland Univ., Rockhampton, Qld., Australia

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

1337

Abstract

The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a neural network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.

Keywords

feature extraction; handwriting recognition; image classification; neural nets; UNIPEN benchmark database; feature extraction technique; neural network classifier; online handwriting recognition; single global feature vector; Australia; Character recognition; Data mining; Feature extraction; Handwriting recognition; Informatics; Information technology; Neural networks; Spatial databases; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380140

Filename

1380140