DocumentCode :
2691507
Title :
A Handwritten Digit Recognition Algorithm using Two-Dimensional Hidden Markov Models for Feature Extraction
Author :
Wierer, J. ; Boston, Nigel
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
Volume :
2
fYear :
2007
fDate :
15-20 April 2007
Abstract :
We propose a handwritten digit recognition algorithm that uses 4×4 2D hidden Markov models to extract basic features from an unclassified image. The novel idea given here is that we use powerful techniques from the emerging mathematical fields of tropical geometry and algebraic statistics to determine parameters for the model. The distance between the unclassified images and prototypes is calculated in stages, where estimates of the distance become finer as obviously distant prototypes are discarded from the pool of possible K-nearest neighbors. Our algorithm achieves a 95.51 percent recognition rate with zero rejection on the MNIST database of handwritten digits.
Keywords :
feature extraction; handwriting recognition; hidden Markov models; matrix algebra; statistics; K-nearest neighbors; algebraic statistics; feature extraction; handwritten digit recognition algorithm; tropical geometry; two-dimensional hidden Markov models; Feature extraction; Geometry; Handwriting recognition; Hidden Markov models; Image databases; Image recognition; Mathematical model; Prototypes; Solid modeling; Statistics; Handwriting recognition; Hidden Markov models; character recognition; feature extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Type :
conf
DOI :
10.1109/ICASSP.2007.366283
Filename :
4217456
Link To Document :
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