Make your own free website on Tripod.com

B. Sc. Thesis

Home
My Book Shelf
About Me
Childhood
My Parents
My Sister
My Fiancée
IT Conferences
Family Photo Album
Academic Research
PresentWorkingEnvironment
B. Sc. Thesis
Guest Book for Visitors
Pathfinders

To view the full report, please click here.

Thesis Abstract
 
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 view the full report, please click here.