Abstract
Recognition of hand-drawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar. It also provides meaningful probabilistic measures of model score, which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the trade-off between data fit and model complexity to be optimized automatically.
Original language | English |
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Title of host publication | Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on |
Pages | 20-25 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Oct 2004 |
Keywords / Materials (for Non-textual outputs)
- Bayes methods
- handwritten character recognition
- object recognition
- temporal databases
- Bayesian model
- generative model
- generative probabilistic framework
- hand drawn shape recognition
- temporal information
- Bayesian methods
- Conferences
- Fitting
- Handwriting recognition
- Ink
- Robustness
- Shape
- USA Councils