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A method for pattern recognition (character recognition), invariant under translation, rotation and scaling, using Artificial
Neural Network (ANN) classifier is presented here. That is, the system is intended to recognize translated, rotated, scaled
or composite versions of the exemplar patterns (character). The method is a combination of several novel algorithms that decomposes
the task of classification into several steps. The classification process is performed upon a set of thinned characters. The
method in this system consists of two parts. The first is a preprocessor and the second is an ANN classifier. The first step
of the method takes into account the invariant properties of the object and extracts topological object characteristics. This
preprocessing phase uses a robust feature set generated by radial coding and differential radial coding schemes. For an ideal
case, rotated or scaled pattern is assumed to be noise free and distortion less. Hence, the outputs of the preprocessor are
invariant to rotation, scaling and translation. However, in practical case rotation and scaling always insert some noise in
the input pattern. The second part of the method is an ANN classifier, which is trained by the backpropagation algorithm.
Outputs of the preprocessor are fed into this classifier. With slight variation, the classifier is expected to classify the
input character patterns correctly. The uniqueness of this approach resulted from the application of the recognition algorithm
on thinned characters. The method is tested to classify English characters. Performance analysis of the model is presented
which shows satisfactory accuracy. The performance also depends on the number of neurons in the hidden layer. So, the effect
of the structure of the Neural Network classifier on the performance is also studied.
To access the full thesis report, please follow this link: B. Sc. Thesis Report |
