Applied some new features in off-line recognition of totally unconstrained handwritten numerals using neural network

Author

Lin, Dong ; Xixian, Chen ; Shanpei, Wu ; Yuanyan, Tang

Author_Institution

Dept. of Telecommun. Eng., Beijing Univ. of Posts & Telecommun., China

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

392

Abstract

Some new features in recognition of totally unconstrained handwritten numerals are applied. The new features are based on image projection and do wavelet transformation, then at the deferent scale, they calculate the projection´s fractal dimension, using the fractal dimension as a feature applied to neural network input. The new features have some advantages: it is rotate invariant, and it can represent the image´s characteristic at deferent scale. In order to verify the performance of the new features, experiments with a handwritten numeral database collected from Beijing Postal center were performed. The correct recognition rate in the training set was 99.55% and in the testing set was 96.5%

Keywords

feature extraction; fractals; handwriting recognition; neural nets; wavelet transforms; Beijing Postal center; deferent scale; fractal dimension; handwritten numeral database; image projection; neural network input; offline recognition; recognition rate; rotate invariant; totally unconstrained handwritten numerals; wavelet transformation; Character recognition; Detectors; Feature extraction; Fractals; Frequency; Handwriting recognition; Image edge detection; Image recognition; Multi-layer neural network; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672807

Filename

672807