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One-dimensional representation of two-dimensional information for HMM based handwriting recognition

In this study, we introduce a one-dimensional feature set, which embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for Hidden Markov Model (HMM) based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed recognition system is tested on handwritten data of the National Institute of Standards and Technology (NIST) database and a local database. The experimental results indicate very high recognition rates.