کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
533034 | 870046 | 2004 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components](/preview/png/533034.png)
چکیده انگلیسی
In off-line handwriting recognition, classifiers based on hidden Markov models (HMMs) have become very popular. However, while there exist well-established training algorithms which optimize the transition and output probabilities of a given HMM architecture, the architecture itself, and in particular the number of states, must be chosen “by hand”. Also the number of training iterations and the output distributions need to be defined by the system designer. In this paper we examine several optimization strategies for an HMM classifier that works with continuous feature values. The proposed optimization strategies are evaluated in the context of a handwritten word recognition task.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 37, Issue 10, October 2004, Pages 2069–2079
Journal: Pattern Recognition - Volume 37, Issue 10, October 2004, Pages 2069–2079
نویسندگان
Simon Günter, Horst Bunke,