C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Crisp and Fuzzy Neural Networks for Handwritten Character Recognition

Paul Gader, Magdi Mohamed, and Jung−Hsien Chiang combine a fuzzy neural network and a crisp neural

network for the recognition of handwritten alphabetic characters. They use backpropagation for the crisp

neural network and a clustering algorithm called K−nearest neighbor for the fuzzy network. Their

consideration of a fuzzy network in this study is prompted by their belief that if some ambiguity is possible in

deciphering a character, such ambiguity should be accurately represented in the output. For example, a

handwritten “u” could look like a “v” or “u.” If present, the authors feel that this ambiguity should be

translated to the classifier output.

Feature extraction was accomplished as follows: character images of size 24x16 pixels were used. The first

stage of processing extracted eight feature images from the input image, two for each direction (north,

northeast, northwest, and east). Each feature image uses an integer at each location that represents the length

of the longest bar that fits at that point in that direction. These are referred to as bar features. Next 8x8

overlapping zones are used on the feature images to derive feature vectors. These are made by taking the

summed values of the values in a zone and dividing this by the maximum possible value in the zone. Each

feature image results in a 15,120 element feature vectors.

Data was obtained from the U.S. Postal Office, consisting of 250 characters. Results showed 97.5% and

95.6% classification rates on training and test sets, respectively, for the neural network. The fuzzy network

resulted in 94.7% and 93.8% classification rates, where the desired output for many characters was set to

ambiguous.