Unconstrained handwritten numeral recognition using Hausdorff distance and multi-layer neural network classifier

Author

Wu, Xuejing ; Shi, Pengfei

Author_Institution

Inst. of Image Process. & Pattern Recognition, Shanghai Jiaotong Univ., China

fYear

1999

fDate

20-22 Sep 1999

Firstpage

249

Lastpage

252

Abstract

A method for zip code recognition is presented. 2D binary images are input to HAVNET, a neural network which employs the Hausdorff distance as a similarity metric to train the weights which are required to represent the patterns learned by the network. A new learning rule for HAVNET is also introduced. In this approach, HAVNET is combined with a multilayer neural network. The Hausdorff distance acts as a zip code filter. Only the confusing digits are input into the multilayer network for training or recognition. Experimental results show that the correct-recognition rate with zero rejection is more than 97% for a database used by the Chinese mail sorting system

Keywords

feedforward neural nets; handwritten character recognition; image classification; learning (artificial intelligence); mailing systems; optical character recognition; 2D binary images; Chinese mail sorting system; HAVNET neural network; Hausdorff distance; OCR; confusing digits; correct-recognition rate; database; filter; learned patterns; learning rule; multilayer neural network classifier; neural weight training; similarity metric; unconstrained handwritten numeral recognition; zero rejection; zip code recognition; Euclidean distance; Handwriting recognition; Image recognition; Multi-layer neural network; Neural networks; Optical character recognition software; Optical filters; Pattern recognition; Postal services; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on

Conference_Location

Bangalore

Print_ISBN

0-7695-0318-7

Type

conf

DOI

10.1109/ICDAR.1999.791771

Filename

791771