Article,
Character preclassification based on genetic programming
Pattern Recognition Letters, 23 (12): 1439--1448 (2002)
DOI: doi:10.1016/S0167-8655(02)00104-6
Abstract
This paper presents a learning system that uses
genetic programming as a tool for automatically
inferring the set of classification rules to be used
during a pre-classification stage by a hierarchical
handwritten character recognition system. Starting from
a structural description of the character shape, the
aim of the learning system is that of producing a set
of classification rules able to capture the
similarities among those shapes, independently of
whether they represent characters belonging to the same
class or to different ones. In particular, the paper
illustrates the structure of the classification rules,
the grammar used to generate them and the genetic
operators devised to manipulate the set of rules, as
well as the fitness function used to drive the
inference process. The experimental results obtained by
using a set of 10,000 digits extracted from the NIST
database show that the proposed pre classification is
efficient and accurate, because it provides at most 6
classes for more than 87% of the samples, and the error
rate almost equals the intrinsic confusion found in the
data set.
