Offline Handwriting Recognition using Genetic Algorithm

TL;DR

This paper presents a novel offline handwriting recognition method using genetic algorithms to generate and match mixed style graphs, achieving 98.44% accuracy.

Contribution

It introduces a genetic algorithm-based approach to recognize handwritten characters by generating intermediate styles through graph mixing.

Findings

Achieved 98.44% recognition accuracy.

Demonstrated effectiveness of genetic algorithms in style variation handling.

Validated method on a pool of character images.

Abstract

Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would be able to recognize characters written in styles intermediate between them or formed by their mixture. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing. Using this method we received an accuracy of 98.44%.