Offline Handwriting Recognition using Genetic Algorithm
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%.
Keywords: Handwriting recognition, generic algorithms, graph
theory, coordinate geometry, offline handwriting recognition,
optical character recognition
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