Hidden Markov models (HMM) are currently widely used and a successful statistical method for automatic recognition of spoken utterances. This paper describes an adaptation of HMM to automatic recognition of unrestricted handwritten words. Focussed on HMM, we describe many interesting details of a 50,000 vocabulary recognition system for US city names. This system includes feature extraction, classification, estimation of model parameters, and word recognition. The feature extraction module transforms a binary image to a sequence of feature vectors. The classification module consists of a transformation based on linear discriminant analysis and gaussian soft-decision vector quantizers which transform feature vectors into sets of symbols and associated likelihoods. Symbols and likelihoods form the input to both HMM training and recognition. HMM training performed in several succeasiire steps requires only a small amount of gestalt labeled data on the level of characters for initialization. Most of the training material must be only labeled as uppercase ascii word. Step-by-step training is necessary because characters may occur in different styles of writing which is taken into account by a sophisticated model topology. HMM recognition based on the Viterbi algorithm runs on subsets of the whole vocabulary. A subset is selected by the ZIP-code recognition module and a statistically-based estimation of the number of characters in the word to be recognized. HMM recognition uses a mued breadth-first, depth-first search technique. It should be further mentioned that OUT recognition algorithm is segmentation-free, i.e. it works directly on lexicon WO& and not on presegmented characters.
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