  Accuracy is the most important parameter among few others which defines the
effectiveness of a machine learning algorithm. Higher accuracy is always
desirable. Now, there is a vast number of well established learning algorithms
already present in the scientific domain. Each one of them has its own merits
and demerits. Merits and demerits are evaluated in terms of accuracy, speed of
convergence, complexity of the algorithm, generalization property, and
robustness among many others. Also the learning algorithms are
data-distribution dependent. Each learning algorithm is suitable for a
particular distribution of data. Unfortunately, no dominant classifier exists
for all the data distribution, and the data distribution task at hand is
usually unknown. Not one classifier can be discriminative well enough if the
number of classes are huge. So the underlying problem is that a single
classifier is not enough to classify the whole sample space correctly. This
thesis is about exploring the different techniques of combining the classifiers
so as to obtain the optimal accuracy. Three classifiers are implemented namely
plain old nearest neighbor on raw pixels, a structural feature extracted
neighbor and Gabor feature extracted nearest neighbor. Five different
combination strategies are devised and tested on Tibetan character images and
analyzed
