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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qizhen He Shijie Chen Mingxi Zhao Wei Lin |
| Copyright Year | 2012 |
| Abstract | In this paper, we propose a hybrid language model for handwritten Chinese sentence recognition. This hybrid model is integrated from several independent language models, each of which is trained from a distinct type of corpus and models specifically the linguistic behavior for that type of corpus. By inferring the type of the string which the user has already written, we can make this hybrid language model contribute more precisely to the recognition engine. Our experiments show that the hybrid language model performs consistently well among different types of handwritten articles, and the overall performance is significantly better than a single standard language model. We also propose a candidate re-ranking process after recognition by reducing the language scores to improve the recognition accuracy. The experiment result also demonstrates that this re-ranking process effectively improves the performance of the recognition engine in terms of accuracy. |
| Starting Page | 129 |
| Ending Page | 134 |
| File Size | 225817 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467322621 |
| DOI | 10.1109/ICFHR.2012.157 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-09-18 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Biological cells Engines Handwriting recognition Character recognition Mathematical model Hidden Markov models Chinese sentence Recognition Language Model Handwritten recognition |
| Content Type | Text |
| Resource Type | Article |
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