High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Fields

In this paper we present a system for the recognition of handwritten words on literal cheque amounts which advantageously combine hmms and Markov random elds mrfs It operates at pixel level in a holistic manner on height normalized word images which are viewed as random eld realizations The hmm analyzes the image along the horizontal writing direction in a speci c state observation probabil ity given by the column product of causal mrf like pixel conditional probabilities Aspects concerning de nition training and recognition via this type of model are developed throughout the paper We report a average word recognition rate on words and a amount rate on amounts of the srtp French postal cheque database words amounts di erent scriptors