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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhou, Baoyao Weihong Wang Zhanghui Chen |
| Copyright Year | 2011 |
| Description | Author affiliation: IBM Research - China (Zhou, Baoyao) || Institute of Semiconductors, Chinese Academy of Sciences, China (Zhanghui Chen) || School of Computer Science, University of Waterloo, Canada (Weihong Wang) |
| Abstract | Creating personal Chinese handwritten font library is a very time-consuming job, with the majority of time spent on users manually writing a large number of Chinese characters. To dramatically cut down the time cost, we propose an efficient solution to generate Chinese handwritten fonts by effectively reusing the sample characters that users write. Our solution first builds a Chinese Character Radical Composition Model based on the images of standard printed characters. The use of contour curve based radical clustering approach facilitates the critical task of learning the model. We then use the model to decide a much smaller set of character that users need to write. The same model is also used to guide the automatic segmentation of user's hand input characters and construction of other characters. Our prototype only needs users to input around 20% characters as usual to create their own qualified handwritten fonts. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 928629 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781612843483 |
| ISSN | 19457871 |
| e-ISBN | 9781612843506 |
| e-ISBN | 9781612843490 |
| DOI | 10.1109/ICME.2011.6011892 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-11 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle separators Libraries Image segmentation Layout Computers Prototypes Character recognition Chinese handwritten font Chinese character structure model Chinese character processing |
| Content Type | Text |
| Resource Type | Article |
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