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One dimensional representation of two dimensional information for HMM based handwritten recognition

In this study, we introduce a new set of one-dimensional discrete, constant length features to represent two dimensional shape information for HMM (Hidden Markov Model), based handwritten optical character recognition problem. The proposed feature set embeds the two dimensional information into a sequence of one-dimensional codes, selected from a code book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for HMM based shape recognition schemes. The new feature set is used in a handwritten optical character recognition scheme, where a sequence of segmentation and recognition stages is employed. The normalization parameters, which maximize the recognition I ate, are dynamically, estimated in the training stage of HMM. The proposed character recognition system is tested on both a locally generated cursively handwritten data and isolated number digits of NIST database. The experimental results indicate high recognition rates.