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Alternatives to Variable Duration HMM in Handwriting Recognition
November 1998 (vol. 20 no. 11)
pp. 1275-1280
ASCII Text
x 
 
Amlan Kundu, Yang He, Mou-Yen Chen, "Alternatives to Variable Duration HMM in Handwriting Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1275-1280, November, 1998.
 
 
BibTex
x
 
@article{ 10.1109/34.730561,
author = {Amlan Kundu and Yang He and Mou-Yen Chen},
title = {Alternatives to Variable Duration HMM in Handwriting Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {20},
number = {11},
issn = {0162-8828},
year = {1998},
pages = {1275-1280},
doi = {http://doi.ieeecomputersociety.org/10.1109/34.730561},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Alternatives to Variable Duration HMM in Handwriting Recognition
IS - 11
SN - 0162-8828
SP1275
EP1280
EPD - 1275-1280
A1 - Amlan Kundu,
A1 - Yang He,
A1 - Mou-Yen Chen,
PY - 1998
KW - Variable duration HMM (VDHMM)
KW - path discriminant HMM (PD-HMM)
KW - model discriminant HMM (MD-HMM)
KW - nonergodic HMM (NEHMM)
KW - variable sequence length HMM (VSLHMM)
KW - adaptive length Viterbi algorithm (ALVA).
VL - 20
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 

Abstract—A successful handwritten word recognition (HWR) system using Variable Duration Hidden Markov Model (VDHMM) and the PD-HMM strategy is easy to implement. The central theme of this paper is to show that if the duration statistics are computed, it could be utilized to implement an MD-HMM approach for better experimental results. This paper also describes a PD-HMM based HWR system where the duration statistics are not explicitly computed, but results are still comparable to VDHMM based HWR scheme [1].

[1] M.Y. Chen, A. Kundu, and S.N. Srihari, “Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition,” IEEE Trans. Image Processing, vol. 4, no. 12, pp. 1675-1688, Dec. 1995.
[2] J.A. Vlontzos and S.Y. Kung, “Hidden Markov Models for Character Recognition,” IEEE Trans. Image Processing, vol. 1, no. 4, pp. 539-543, 1989.
[3] S.S. Kuo and O. Agazzi, “Keyword Spotting in Poorly Printed Documents Using Pseudo 2-D Hidden Markov Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 8, pp. 842-848, Aug. 1994.
[4] M. Mohammed and P. Gader, “Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 5, pp. 548-554, May 1996.
[5] H. Bunke, M. Roth, and E.G. Schukat-Talamazzini, "Off-Line Cursive Handwritten Word Recognition Using Hidden Markov Models," Pattern Recognition J., vol. 28, no. 9, pp. 1,399-1,413, 1995.

Index Terms:
Variable duration HMM (VDHMM), path discriminant HMM (PD-HMM), model discriminant HMM (MD-HMM), nonergodic HMM (NEHMM), variable sequence length HMM (VSLHMM), adaptive length Viterbi algorithm (ALVA).
Citation:
Amlan Kundu, Yang He, Mou-Yen Chen, "Alternatives to Variable Duration HMM in Handwriting Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1275-1280, Nov. 1998, doi:10.1109/34.730561
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