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

In this study, we introduce a set of one-dimensional features to represent two dimensional shape information for HMM (Hidden Markov Model) based handwritten optical character recognition problem. The proposed feature set 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 HMM based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed character recognition system is tested on handwritten data of the NIST database and a local database. The experimental results indicate very high recognition rates.