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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rodriguez-Serrano, J.A. Perronnin, F. |
| Copyright Year | 2009 |
| Abstract | We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained “on the fly” with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram(LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models(SC-HMM), in which the typed-text models are constrained to a “vocabulary” of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SCHMM is crucial. To the best of the authors’ knowledge, this is the first work to address this issue in a non-trivial manner. |
| Starting Page | 351 |
| Ending Page | 355 |
| File Size | 268222 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445004 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2009.201 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-26 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image retrieval Hidden Markov models Prototypes Shape Robustness Pattern analysis Handwriting recognition Rendering (computer graphics) Writing Text analysis hidden Markov models word spotting handwriting recognition word retrieval local gradient histogram features |
| Content Type | Text |
| Resource Type | Article |
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