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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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Submitted on : Friday, October 20, 2006 - 1:35:57 PM
Last modification on : Monday, June 20, 2016 - 2:10:32 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 8:16:50 PM


  • HAL Id : inria-00108307, version 1



Marcus Liwicki, Horst Bunke. HMM-Based On-Line Recognition of Handwritten Whiteboard Notes. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00108307⟩



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