Recognition of broken characters from historical printed books using Dynamic Bayesian Networks

This paper investigates the application of dynamic Bayesian networks (DBNs) to the recognition of degraded characters from historical printed books. This framework allows us to capture the 2D nature of character images by the coupling of two HMMs (Hidden Markov Models). The vertical HMM observes image columns while the horizontal HMM observes image rows respectively. Two coupled DBN architectures are proposed to model interactions between these two streams. We present experiments on real degraded characters extracted from an ancient printed book (17th century). These experiments demonstrate that coupled architectures significantly better cope with broken characters than non coupled ones and than discriminative methods such as SVMs.