The three basic problems of two-dimensional (2-D) hidden Markov models (HMMs) are studied

including probability evaluation

optimal states and parameter estimation.By using the idea that the sequences of states on columns or rows of a 2-D HMM can be seen as states of a 1-D HMM

several new analytic formulae for solving these three problems are theoretically derived and further demonstrated by computer simulation.