  This Paper presents a method for lexicon reduction of Printed Farsi subwords
based on their holistic shape features. Because of the large number of Persian
subwords variously shaped from a simple letter to a complex combination of
several connected characters, it is not easy to find a fixed shape descriptor
suitable for all subwords. In this paper, we propose to select the descriptor
according to the input shape characteristics. To do this, a neural network is
trained to predict the appropriate descriptor of the input image. This network
is implemented in the proposed lexicon reduction system to decide on the
descriptor used for comparison of the query image with the lexicon entries.
Evaluating the proposed method on a dataset of Persian subwords allows one to
attest the effectiveness of the proposed idea of dealing differently with
various query shapes.
