Optical Character Recognition Program for Images of Printed Text using a Neural Network

In this paper we present a simple method using a self-organizing map neural network (SOM NN) which can be used for character recognition tasks. It describes the results of training a SOM NN to perform optical character recognition on images of printed characters. 49 features have been used to distinguish between 62 characters (both uppercase and lowercase letters of the English language and numerals). The implemented program recognizes text by analyzing an image file. The text to be recognized is currently limited to characters typed using the Verdana font type, bolded with a font size of 18. The program is capable of handling non-ideal images (noisy, colored text, rotated image). Recognition accuracy is consistently 100% for ideal images, but ranges between 80% -100% for non-ideal images.