Document Type


Date of Award


Degree Name

Master of Science in Computer and Information Science - (M.S.)


Computer and Information Science

First Advisor

David T. Wang


This report presents two algorithms for text recognition. One is a neural-based orthogonal vector with pseudo-inverse approach for pattern recognition. A method to generate N orthogonal vectors for an N-neuron network is also presented. This approach converges the input to the corresponding orthogonal vector representing the prototype vector. This approach can restore an image to the original image and thus has error recovery capacility. Also, the concept of sub-networking is applied to this approach to enhance the memory capacity of the neural network. This concept drastically increases the memory capacity of the network and also causes a reduction of the convergence time to stable states. Another approach is to use the Levenshtein algorithm for string matching following the application of rules to recognise a given character. Both these methods are discussed and the results are presented.



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