Bayesian Classifier Based Letter Recognition

Bayesian Classifier Based Letter Recognition
Authors : Twaha Kabika
Publication Date: 28-02-2013


Author(s):  Twaha Kabika

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol.2 - Issue 2 (February - 2013)

e-ISSN:   2278-0181


ABSTRACT: The Main task of this project is to design a Bayesian classifier, which would distinguish between two letters K={'A', 'C'}, and that using only a single measurement: x = (sum of pixel intensities in the left half of the image) - (sum of pixel intensities in the right half of the image), This measurement (so-called feature) assigns a real number to each image. In this report the assumption is percentage of letters 'A' and 'C' in a given dataset is known, i.e. we know the a priori probabilities p(A) and p(C). Further, Also another assumption is conditional probabilities p(x|A) and p(x|C) are also known, that is the probabilities that a value x is measured if the letter is 'A' or 'C' respectively. Probabilities p(x|A) and p(x|C) are Gaussian distributions with mean value and variance given for each class. In this problem, the set of decisions D coincides with the set of hidden states K and the loss function W takes just two values: 0 for correctly recognized and 1 for incorrectly recognized letter. In this special case the classificator q expresses as q(x) = argmax_k p(k|x) = argmax_k p(k|x)/p(x) = argmax_k p(k|x) = argmax_k p(k) p(x|k), Where p(k|x) is called a posteriori probability of class k given the measurement x. Symbol argmax_k denotes finding k maximizing the argument.


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