Neural Network Recognition System for Igbo Vowels

Handwritten recognition is one of the problems facing researchers in the field of computer vision that many researchers are still finding it difficult to provide solution. Several researches have been carried out on English Language and Asian Character recognition with little work done on the African language character recognition. In this paper, we consider Igbo vowels character recognition; we employed several people to write Igbo vowels character with their handwritten. These characters differ in their shapes, size, and orientation. These characters are then resampled by rotating each one of them, this make the system to be more intelligent. In this work, the process of recognizing the Igbo vowel character was divided into two stages. In the first stage, preprocessing of the characters was carried out by converting the characters into binary using the threshold value obtained from the Otsu’s method. After that noise was removed by filtering. In the second stage, the feature extraction was carried out to bring out the important features of the image. These were further resized and reshaped to be fed into the neural network. The multilayer feedforward neural network was created and trained using backpropagation algorithms. After the training has been carried out, the network was simulated. The result obtained from the simulation gave a recognition rate of 90.2%.