Fusion of multiple handwritten word recognition techniques

Author

Verma, B. ; Gader, P.

Author_Institution

Sch. of Inf. Technol., Griffith Univ., Brisbane, Qld., Australia

Volume

2

fYear

2000

fDate

2000

Firstpage

926

Abstract

Fusion of multiple handwritten word recognition techniques is described. A novel borda count for fusion based on ranks and confidence values is proposed. Three techniques with two different conventional segmentation algorithms in conjunction with backpropagation and radial basis function neural networks have been used in this research. Development has taken place at the University of Missouri and Griffith University. All experiments were performed on real-world handwritten words taken from the CEDAR benchmark database. The word recognition results are very promising and highest (91%) among published results for handwritten words

Keywords

backpropagation; handwritten character recognition; image segmentation; optical character recognition; radial basis function networks; CEDAR benchmark database; backpropagation; borda count; confidence values; experiments; handwritten word recognition; image segmentation algorithms; radial basis function neural networks; ranks; Australia; Character recognition; Computer science; Databases; Fuses; Gold; Handwriting recognition; Information technology; Neural networks; Postal services;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop

Conference_Location

Sydney, NSW

ISSN

1089-3555

Print_ISBN

0-7803-6278-0

Type

conf

DOI

10.1109/NNSP.2000.890173

Filename

890173