Name
Abstract | ||
|---|---|---|
We study models that characterize pen trajectories of online handwritten characters in a fine manner. We propose radical based fine trajectory hidden Markov models (HMMs), which adopt radicals as basic units, and a multi-path HMM topology that emits observations with multi-space distributions (MSD) is built for each radical. Meanwhile, various stroke orders, writing styles and realness of sub-strokes are reasonably modeled. The radical based fine trajectory HMMs lead to handwriting recognition with effective prediction, and their generative nature can be utilized for a novel handwriting synthesis framework. Experimental show that along with the model precision increasing, about 50% recognition error can be reduced, and the fine models can generate decent character samples. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761826 | ICPR |
Keywords | Field | DocType |
handwriting synthesis framework,multispace distributions,radical based fine trajectory hmm,multipath hmm topology,topology,handwritten character recognition,online handwritten characters,hidden markov models,hidden markov model,visualization,trajectory,handwriting recognition,writing | Pattern recognition,Handwriting,Intelligent character recognition,Character recognition,Computer science,Visualization,Handwriting recognition,Speech recognition,Artificial intelligence,Hidden Markov model,Trajectory | Conference |
ISSN | ISBN | Citations |
1051-4651 E-ISBN : 978-1-4244-2175-6 | 978-1-4244-2175-6 | 2 |
PageRank | References | Authors |
0.37 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Liu | 1 | 38 | 5.69 |
| Lei Ma | 2 | 46 | 2.77 |
| Frank K. Soong | 3 | 1395 | 268.29 |