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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sung-Jung Cho Perrone, M. Ratzlaff, E. |
| Copyright Year | 2003 |
| Description | Author affiliation: IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Sung-Jung Cho; Perrone, M.; Ratzlaff, E.) |
| Abstract | This paper presents a new probability table compression method based on mixture models applied to N-tuple recognizers. Joint probability tables are modeled by lower dimensional probability mixtures and their mixture coefficients. The maximum likelihood parameters of the mixture models are trained by the expectation-maximization (EM) algorithm and quantized to one byte integers. The probability elements which mixture models do not estimate reliably are kept separately. Experimental results with on-line handwritten UNIPEN digits show that the total memory size of an N-tuple recognizer is reduced from 11.8 Mbytes to 0.55 Mbytes, while the recognition rate drops from 97.7% to 97.5%. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 272225 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1202491 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image coding Handwriting recognition Quantization Maximum likelihood estimation Handheld computers Personal digital assistants Real time systems Training data Character recognition Character generation |
| Content Type | Text |
| Resource Type | Article |
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