A feature for character recognition based on directional distance distributions

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 an standard database have been reported, there is stiff room to improve the recognition rate by developing an improved feature, fn this paper, we propose a new feature based on DDD (Directional Distance Distribution) information. This new concept regards the input pattern army as being circular, Also if contains very rich information by encoding in one representation both the white/black distribution and the directional distance distribution. A test performed off the CENPARMI handwritten numeral database showed a promising result of 97.3% recognition with a neural network classifier using the DDD feature.