Optimizing Binary Feature Vector Similarity Measure using Genetic Algorithm and Handwritten Character Recognition

Classifying an unknown input is a fundamental problem in pattern recognition. A common method is to define a distance metric between patterns and find the most similar pattern in the reference set. When patterns are in binary feature vector form, there have been two approaches to improve the performance over the equal-weighted Hamming distance metric. One is to give different weights to different features using an optimization technique, and the other is to use a similarity measure that gives full credit to features present in both patterns and the less credit to those absent from both patterns. Both approaches have been reported to perform better than the naı̈ve Hamming distance approach. In this paper, we propose to combine these two approaches using a genetic algorithm to optimize weights. Experimental results show that this method is superior to conventional measures in an OCR application.