Hidden Markov Models (HMMs) can model the similarity and variation among samples of a class through a doubly stochastic process. The main difficulty of its application to off-line recognition of cursive words is to produce a consistent sequence of feature vectors from the input word image. In conventional HMM based methods, a sequence of thin fixed-width vertical frames are extracted as feature vectors from the image. The extracted feature is sensitive to the error of the preprocessing step e.g. baseline detection. In this paper we present an HMM based modeling approach together with an extended sliding window feature extraction method to decrease the influence of the baseline detection error. Experiments have been carried out and show that our novel approach can achieve better recognition performance and reduce the relative error rate significantly compared with traditional methods.
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