Abstract: A method is described wherein the dual representation mathematical principle is used for the design of decision systems. This principle permits some decision functions that are weighted sums of predefined functions to be represented as memory-based decision function. Using this principle a memory-based decision system with optimum margin is designed wherein weights and prototypes of training patterns of a memory-based decision function are determined such that the corresponding dual decision function satisfies the criterion of margin optimality.

Abstract: A time delay neural network is defined having feature detection layers which are constrained for extracting features and subsampling a sequence of feature vectors input to the particular feature detection layer. Output from the network for both digit and uppercase letters is provided by an output classification layer which is fully connected to the final feature detection layer. Each feature vector relates to coordinate information about the original character preserved in a temporal order together with additional information related to the original character at the particular coordinate point. Such additional information may include local geometric information, local pen information, and phantom stroke coordinate information relating to connecting segments between the end point of one stroke and the beginning point of another stroke.The network is also defined to increase the number of feature elements in each feature vector from one feature detection layer to the next.