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Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields
July 2009 (vol. 31 no. 7)
pp. 1184-1194
ASCII Text
x 
 
Huaigu Cao, Venu Govindaraju, "Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1184-1194, July, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.126,
author = {Huaigu Cao and Venu Govindaraju},
title = {Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {7},
issn = {0162-8828},
year = {2009},
pages = {1184-1194},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.126},
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 - Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields
IS - 7
SN - 0162-8828
SP1184
EP1194
EPD - 1184-1194
A1 - Huaigu Cao,
A1 - Venu Govindaraju,
PY - 2009
KW - Markov random field
KW - image segmentation
KW - document analysis
KW - handwriting recognition.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Huaigu Cao, University at Buffalo, Amherst
Venu Govindaraju, University at Buffalo, Amherst
This paper presents a statistical approach to the preprocessing of degraded handwritten forms including the steps of binarization and form line removal. The degraded image is modeled by a Markov Random Field (MRF) where the hidden-layer prior probability is learned from a training set of high-quality binarized images and the observation probability density is learned on-the-fly from the gray-level histogram of the input image. We have modified the MRF model to drop the preprinted ruling lines from the image. We use the patch-based topology of the MRF and Belief Propagation (BP) for efficiency in processing. To further improve the processing speed, we prune unlikely solutions from the search space while solving the MRF. Experimental results show higher accuracy on two data sets of degraded handwritten images than previously used methods.

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Index Terms:
Markov random field, image segmentation, document analysis, handwriting recognition.
Citation:
Huaigu Cao, Venu Govindaraju, "Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1184-1194, July 2009, doi:10.1109/TPAMI.2008.126
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