Sketched symbol recognition using Zernike moments

Author

Hse, Heloise ; Newton, A. Richard

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA

Volume

1

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

367

Abstract

We present an on-line recognition method for hand-sketched symbols. The method is independent of stroke-order, -number, and -direction, as well as invariant to scaling, translation, rotation and reflection of symbols. Zernike moment descriptors are used to represent symbols and three different classification techniques are compared: support vector machines (SVM), minimum mean distance (MMD), and nearest neighbor (NN). We have obtained a 97% recognition accuracy rate on a dataset consisting of 7,410 sketched symbols using Zernike moment features and a SVM classifier.

Keywords

Zernike polynomials; handwritten character recognition; pattern classification; support vector machines; SVM classifier; Zernike moment descriptors; data acquisition; hand sketched symbols; minimum mean distance; nearest neighbor; online recognition method; recognition accuracy rate; support vector machines classifier; Character recognition; Image recognition; Nearest neighbor searches; Neural networks; Reflection; Robustness; Shape; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334128

Filename

1334128