Artificial Neural Network Classification for Handwritten Digits Recognition

Abstract

Handwritten recognition is very powerful technology to support many applications comes in the forefront of automated sorting of
letters and bank checks, and help the blind and Who have difficulty to read books and magazines, and the translation of books from one language
to another, and converted to texts can store and processed in the computer. This paper is present two artificial neural network classification for
handwritten digit recognition (from 0 to 9) with accuracy more than 98% by using an application of feed-forward multilayer neural network with
two different classifiers (Forward Multilayer Neural Network FMNN and Binary Coding Neural Network BCNN).The highest recognition
reliability and minimal error rate for the recognition of handwritten digits have been achieved. The back propagation algorithm minimizing the
total error of the network over a set of training by searching of the weight value that achieves the objective. Binary coding approach is used to
reducing the number of output that it leads to reducing the time that need for processing and saving the resources and finally reduce the
network’s complexity.

Keywords: Artificial neural network, handwritten digits recognition, Back propagation, forward multilayer neural network, classifier