Recognition of handwritten words using stochastic models

The paper deals with the global recognition of a small lexicon of words, based on a pseudo segmentation stage introducing anchor points. We avoid the difficult problem of segmentating the word into letters and the complexity involved by such models to build possible letter graphs. We use two structural representations of the word, strokes and graphemes, each of them being analyzed using a Markov model. These simple models are individually optimized by a rigorous choice of the order for fitting the structural properties of the observed data using Akaike information criteria. The conditional probability to have a word model, given the observation sequence, is computed by taking into account the length of the sequence. Results of the study are presented on French cheque images.

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