The thesis goal is to develop a computer system for hand printed digit recognition based on an investigation into various feature extractors and neural network strategies. Features such as subwindow pixel summation, moments, and orientation vectors will be among those investigated. Morphological thinning of characters prior to feature extraction will be used to assess the impact on network training and testing. Different strategies for implementing a multilayer perceptron neural network will be investigated. A high-level language called MatLab will be used for neural network algorithm development and quick prototyping. The feature extractors will be developed to operate on small (less than or equal to 256 bits) binary hand printed digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9).
Library of Congress Subject Headings
Neural networks (Computer science); Computer vision; Image processing--Digital techniques; Pattern recognition systems
Department, Program, or Center
Computer Engineering (KGCOE)
Pink, Jeffrey R., "Features and neural net recognition strategies for hand printed digits" (1995). Thesis. Rochester Institute of Technology. Accessed from
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