Perturbation Models: A New Approach to Improving Handwriting Recognition

This paper discusses the application of the perturbation method to handwriting recognition. First the standard pattern recognition operations of preprocessing, feature extraction, and classification are reviewed. Then we introduce the perturbation method as a new approach to overcoming the problems that result from the sequential architecture traditionally found in pattern recognition systems. Two case studies in handwriting recognition, namely, isolated numeral recognition and cursive handwriting recognition, are presented. Experimental results show that the perturbation method significantly improves the recognition rates of state-of-the-art systems.

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