A comparison of feedforward and self-organizing approaches to the font orientation problems

The problem of determining the orientation of printed text is considered. The problem differs considerably from traditional optical character recognition, and its application to automatic inspection requires efficient processing and highly accurate results. Two methods are described. The first is a feedforward network, with structure and parameters derived using optimal detection theory. The second method makes use of the learning vector quantization self-organizing networks of T. Kohonen (Self-organization and Associative Memory, Springer-Verlag, 1988). Experimental results and a complete implementation are described. Both techniques are found to be successful and their relative advantages are discussed.<>