Combining HMMs for the recognition of noisy printed characters

In this paper, a novel method is proposed for the recognition of noisy printed characters. The method is based on the representation of the shape of a character by two Hidden Markov Models. Recognition is achieved by scoring these models against the test pattern and combining the results. The method has been evaluated using Baird's noise model, producing a peak performance of 99.5% on the test set in the presence of near-minimal noise. The method generalises to recognise characters with noise levels greater than those included in the training set, and an investigation of the top-k performance suggests that a much higher recognition rate could be achieved on language text using a context driven word recogniser.