Handwritten Indian numerals recognition system using probabilistic neural networks

This paper presents a system for the recognition of the handwritten Indian numerals one to nine (1–9) using a probabilistic neural network (PNN) approach. The process involved extracting a feature vector to represent the handwritten digit based on the center of gravity and a set of vectors to the boundary points of the digit object. The feature vector is scale-, translation-, and rotation-invariant. The extracted feature vector is fed to a PNN, which in turn classifies it as one of the nine digits. A set of experiments were conducted to test the performance of the system under different angles between the vectors from the centroid to the boundary of the digit object. A 30° angle results in a 99.72% recognition rate with a short feature vector of 12 entries. This study is meant to be a seed toward building a recognition system for Arabic language characters.