An HMMRF-based statistical approach for off-line handwritten character recognition

We propose a new methodology for off-line handwritten character recognition using a 2D hidden Markov mesh random field (HMMRF)-based statistical approach. In the HMMRF model for character recognition, the inputs to the model are assumed to be sequences of discrete symbols chosen from a finite alphabet. In the proposed methodology, the grey-level input image is first divided into nonoverlapping blocks with same size. Then, each block is encoded into a discrete symbol based on the local features of the block by using the vector quantizer. The HMMRF-based statistical approach necessitates two phases: the decoding phase and the training phase. In both phases we use the lookahead scheme based on a maximum, marginal a posteriori probability criterion for a third-order HMMRF model. In order to verify the performance of the proposed methodology for off-line handwritten character recognition, a large-set handwritten Hangul database was used. Experimental results revealed the viability of the HMMRF-based statistical approach on the task of off-line handwritten character recognition