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Proceedings ArticleDOI

Clustering and Classification for Chinese Character Recognition

Bor-Shenn Jeng, +5 more
- Vol. 1199, pp 1324-1331
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TLDR
The paper evaluates the applicability and results of several clustering and classification algorithms for optical Chinese character recognition, including k-means clustering algorithms, Neural Nets classification, and Hidden Markov Model matching scheme.
Abstract: 
The paper evaluates the applicability and results of several clustering and classification algorithms for optical Chinese character recognition. Emphases are laid on k-means clustering algorithms, Neural Nets classification, and Hidden Markov Model matching scheme. Some experimental results of the algorithms are also presented.

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References
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Lawrence R. Rabiner, +1 more
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TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
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An introduction to computing with neural nets

Richard P. Lippmann
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TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI

Experiments on neural net recognition of spoken and written text

D.J. Burr
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TL;DR: Results indicate that neural networks and nearest-neighbor classifiers perform at near the same level of accuracy and a novel handwriting encoder is described.
Journal ArticleDOI

Research on Machine Recognition of Handprinted Characters

Shunji Mori, +2 more
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TL;DR: The work is described systematically and analyzed in terms of so-called feature matching, which is likely to be the mainstream of the research and development of machine recognition of handprinted Chinese characters.
Proceedings ArticleDOI

A hierarchical system for character recognition with stochastic knowledge representation

Vlontzos, +1 more
TL;DR: A hierarchical system that uses the hidden Markov model (HMM) methodology to represent both general knowledge about objects and knowledge about their possible instantiations is described.