Context-dependent HMM modeling using tree-based clustering for the recognition of handwritten words

Anne-Laure Bianne; Christopher Kermorvant; Laurence Likforman-Sulem

doi:10.1117/12.838806

18 January 2010 Context-dependent HMM modeling using tree-based clustering for the recognition of handwritten words

Anne-Laure Bianne, Christopher Kermorvant, Laurence Likforman-Sulem

Proceedings Volume 7534, Document Recognition and Retrieval XVII; 75340I (2010) https://doi.org/10.1117/12.838806
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

This paper presents an HMM-based recognizer for the off-line recognition of handwritten words. Word models are the concatenation of context-dependent character models (trigraphs). The trigraph models we consider are similar to triphone models in speech recognition, where a character adapts its shape according to its adjacent characters. Due to the large number of possible context-dependent models to compute, a top-down clustering is applied on each state position of all models associated with a particular character. This clustering uses decision trees, based on rhetorical questions we designed. Decision trees have the advantage to model untrained trigraphs. Our system is shown to perform better than a baseline context independent system, and reaches an accuracy higher than 74% on the publicly available Rimes database.

Citation Download Citation

Anne-Laure Bianne, Christopher Kermorvant, and Laurence Likforman-Sulem "Context-dependent HMM modeling using tree-based clustering for the recognition of handwritten words", Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340I (18 January 2010); https://doi.org/10.1117/12.838806

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