KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

This paper presents an intelligent recognition sys-tem for handwritten Pashto letters. However, handwritten char-acter recognition is challenging due to the variations in shape and style. In addition to that, these characters naturally vary among individuals. The identification becomes even daunting due to the lack of standard datasets comprising of 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. Furthermore, the recognition framework extracts zoning features followed by K-Nearest Neighbour (KNN) and Neural Network (NN) for classifying individual letters. Based on the evaluation, the proposed system achieves an overall classification accuracy of approximately 70.05% by using KNN, while an accuracy of 72% through NN at the cost of an increased computation time.