Handwritten Character Recognition Based on 13-point Feature of Skeleton and Self-Organizing Competition Network

Author

Zhong, Chongliang ; Ding, Yalin ; Fu, Jinbao

Author_Institution

Changchun Inst. of Opt., Fine Mech. & Phys., Chinese Acad. of Sci., Changchun, China

Volume

2

fYear

2010

fDate

11-12 May 2010

Firstpage

414

Lastpage

417

Abstract

This article is about the recognition for handwritten characters. Firstly, we use some conventional methods to preprocess the images and introduce the 13-point feature of skeleton method to extract the data that containing feature of the handwritten character. Secondly, we use the data to train the self-organizing competition network. In the end, we test the net and conclude that this method has a good performance at handwritten character recognition.

Keywords

feature extraction; handwritten character recognition; self-organising feature maps; data extraction; handwritten character recognition; self-organizing competition network; skeleton feature; Artificial neural networks; Character recognition; Feature extraction; Handwriting recognition; Image segmentation; Optical character recognition software; Optical fiber networks; Pattern recognition; Physics; Skeleton; 13-point feature of skeleten; ANN; character recognition; image processing; self-organizing competition network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.837

Filename

5522927