  In this paper, we introduce a new modeling approach of texts for handwriting
recognition based on syllables. We propose a supervised syllabification
approach for the French and English languages for building a vocabulary of
syllables. Statistical n-gram language models of syllables are trained on
French and English Wikipedia corpora. The handwriting recognition system, based
on optical HMM context independent character models, performs a two pass
decoding, integrating the proposed syllabic models. Evaluation is carried out
on the French RIMES dataset and English IAM dataset by analyzing the
performance for various coverage of the syllable models. We also compare the
syllable models with lexicon and character n-gram models. The proposed approach
reaches interesting performances thanks to its capacity to cover a large amount
of out of vocabulary words working with a limited amount of syllables combined
with statistical n-gram of reasonable order.
