  This paper presents a recognition system for handwritten Pashto letters.
However, handwritten character recognition is a challenging task. These letters
not only differ in shape and style but also vary among individuals. The
recognition becomes further daunting due to the lack of standard datasets for
inscribed Pashto letters. In this work, we have designed a database of moderate
size, which encompasses a total of 4488 images, stemming from 102
distinguishing samples for each of the 44 letters in Pashto. The recognition
framework uses zoning feature extractor followed by K-Nearest Neighbour (KNN)
and Neural Network (NN) classifiers for classifying individual letter. Based on
the evaluation of the proposed system, an overall classification accuracy of
approximately 70.05% is achieved by using KNN while 72% is achieved by using
NN.
