A feature for character recognition based on directional distance distributions

Author

Oh, Il-Seok ; Suen, Ching Y.

Author_Institution

Dept. of Comput. Sci., Chonbuk Nat. Univ., Chonju, South Korea

Volume

1

fYear

1997

fDate

18-20 Aug 1997

Firstpage

288

Abstract

The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on a standard database have been reported, there is still room to improve the recognition rate by developing an improved feature. The authors propose a new feature based on DDD (directional distance distribution) information. This new concept regards the input pattern array as being circular. It also contains very rich information by encoding in one representation both the white/black distribution and the directional distance distribution. A test performed on the CENPARMI handwritten numeral database showed a promising result of 97.3% recognition with a neural network classifier using the DDD feature

Keywords

character recognition; feature extraction; image classification; neural nets; CENPARMI handwritten numeral database; character recognition; circular input pattern array; directional distance distributions; encoding; features; neural network classifier; recognition rate; white/black distribution; Buildings; Character recognition; Classification algorithms; Distributed computing; Encoding; Neural networks; Performance evaluation; Spatial databases; Standards development; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on

Conference_Location

Ulm

Print_ISBN

0-8186-7898-4

Type

conf

DOI

10.1109/ICDAR.1997.619858

Filename

619858