The random forest (RF) classifier is an ensemble
classifier derived from decision tree idea. However the parallel
operations of several classifiers along with use of randomness
in sample and feature selection has made the random forest a
very strong classifier with accuracy rates comparable to most
of currently used classifiers. Although, the use of random
forest on handwritten digits has been considered before, in
this paper RF is applied in recognizing Persian handwritten
characters. Trying to improve the recognition rate, we suggest
converting the structure of decision trees from a binary tree
to a multi branch tree. The improvement gained this way
proves the applicability of the idea.