One view of the methodology in handwriting character recognition

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Abstract
The paper deals with the general methodology of structural handwriting character recognition systems. It is based on personal observations in developing such systems for commercial and postal applications. A model of handwriting is formulated as a human to human communication model and various implications of this model for handwriting recognition algorithms are considered including optimal criteria for digitization, size, and representativeness of training database and ultimate performance level. Arguments are given in favor of considering handwritten characters as a general class of geometrical curves bounded by some natural constraints as opposed to considerably redundant character shapes typical for machine printed characters. This approach favors a generative model of character formation rather than a transformative model and leads to a natural description of character shapes. Features and feature selection criteria are presented based on psychophysiological and information-theoretic ideas. Significance of classification technique is examined. The advantages and limitations of artificial neural networks for handwritten character recognition are briefly discussed. Various aspects of the complexity of character recognition algorithms are exposed.

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