C++ Neural Networks and Fuzzy Logic: Preface

Writing Style Recognition

J. Nellis and T. Stonham developed a neural network character recognition system that adapts dynamically to

a writing style.

They use a hybrid neural network for hand−printed character recognition, that integrates image processing and

neural network architectures. The neural network uses random access memory (RAM) to model the

functionality of an individual neuron. The authors use a transform called the five−way image processing

transform on the input image, which is of size 32x32 pixels. The transform converts the high spatial frequency

data in a character into four low frequency representations. What they achieve by this are position invariance,

and a ratio of black to white pixels approaching 1, rotation invariance, and capability to detect and correct

breaks within characters. The transformed data are input to the neural network that is used as a classifier and

is called a discriminator.

A particular writing style that has less variability and therefore fewer subclasses is needed to classify the style.

Network size will also reduce confusion, and conflicts lessen.