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We present in this paper a new approach based on multi-stream hidden Markov models (HMM) for the recognition of off-line handwriting. Every word is presented by two HMM models: the first one is learned with features extracted from upper contour, the second with features extracted from lower contour. The combination of these two sources of information is studied using the multi-stream framework. We present experiment results obtained on a database composed of isolated words extracted from incoming mail documents.

A Multi-stream Approach to Off-Line Handwritten Word Recognition