We present a stochastic framework for the recognition of binary random patterns which advantageously combine HMMs and Markov random fields (MRFs). The HMM component of the model analyzes the image along one direction, in a specific state observation probability given by the product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. Experiments were performed on handwritten digits and words in a small lexicon. For the latter, we report a 89.68% average word recognition rate on the SRTP French postal cheque database (7057 words, 1779 scriptors)