HMM-Based On-Line Recognition of Handwritten Whiteboard Notes

Abstract : In this paper we present an on-line recognition system for handwritten texts acquired from a whiteboard. This input modality has received relatively little attention in the handwriting recognition community in the past. The system proposed in this paper uses state-of-the-art normalization and feature extraction strategies to transform a handwritten text line into a sequence of feature vectors. Additional preprocessing techniques are introduced, which significantly increase the word recognition rate. For classification, Hidden Markov Models are used together with a statistical language model. In writer independent experiments we achieved word recognition rates of 67.3% on the test set when no language model is used, and 70.8% by including a language model.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Dernière modification le : lundi 20 juin 2016 - 14:10:32
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  • HAL Id : inria-00108307, version 1

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Marcus Liwicki, Horst Bunke. HMM-Based On-Line Recognition of Handwritten Whiteboard Notes. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00108307〉

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