  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%.
