A neural network structure for feature extraction and recognition of handwritten digits

Author

Zhang, Liming ; Qu, Donghui

Author_Institution

Dept. of Electron. Eng., Fudan Univ., Shanghai, China

fYear

1991

fDate

16-17 Jun 1991

Firstpage

294

Abstract

The authors suggest some criteria of feature extraction for distinguishing numeric handwritten character. According to the criteria, 27-dimension feature vectors are chosen from a training set. Thus some of them can be obtained by neural networks. A back-propagation network is used to classify a test set. The recognition rate performance on 2000 characters written by 200 people is 94%

Keywords

character recognition; computerised pattern recognition; neural nets; back-propagation network; feature extraction; handwritten digits; neural network structure; training set; Artificial neural networks; Cellular neural networks; Character recognition; Constraint theory; Feature extraction; Handwriting recognition; Nearest neighbor searches; Neural networks; Testing; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991. Conference Proceedings, China., 1991 International Conference on

Conference_Location

Shenzhen

Type

conf

DOI

10.1109/CICCAS.1991.184343

Filename

184343

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2884949