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2010 12th International Conference on Frontiers in Handwriting Recognition
Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching
Kolkata, India
November 16-November 18
ISBN: 978-0-7695-4221-8
ASCII Text
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Kiran Bhuvanagiri, Aditya Vikram Daga, Sitaram Ramachandrula, Suryaprakash Kompalli, "Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching," Frontiers in Handwriting Recognition, International Conference on, pp. 283-288, 2010 12th International Conference on Frontiers in Handwriting Recognition, 2010.
 
 
BibTex
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@article{ 10.1109/ICFHR.2010.51,
author = {Kiran Bhuvanagiri and Aditya Vikram Daga and Sitaram Ramachandrula and Suryaprakash Kompalli},
title = {Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching},
journal ={Frontiers in Handwriting Recognition, International Conference on},
volume = {0},
year = {2010},
isbn = {978-0-7695-4221-8},
pages = {283-288},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICFHR.2010.51},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Frontiers in Handwriting Recognition, International Conference on
TI - Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching
SN - 978-0-7695-4221-8
SP283
EP288
A1 - Kiran Bhuvanagiri,
A1 - Aditya Vikram Daga,
A1 - Sitaram Ramachandrula,
A1 - Suryaprakash Kompalli,
PY - 2010
KW - symbol recognition
KW - symbol spotting
KW - sub-graph isomorphism
KW - sub-graph matching
VL - 0
JA - Frontiers in Handwriting Recognition, International Conference on
ER -
 
In this paper we address the problem of hand-drawn symbol spotting in document images. We use stochastic graphical models (SGMs) to represent the structure and variations of hand-drawn symbols. We use a framework which first carries out segmentation and graph formation of the input image, followed by sub-graph matching for spotting of hand-drawn symbols. We used SGMs in place of sub-graphs in a semi-definite programming based sub-graph matching to do the spotting. The experimental results validate our framework. We were able to spot hand-drawn symbols from 10 classes with 78.89% accuracy in a database of 76 document images and also were able to deal with confusingly similar symbol classes.
Index Terms:
symbol recognition, symbol spotting, sub-graph isomorphism, sub-graph matching
Citation:
Kiran Bhuvanagiri, Aditya Vikram Daga, Sitaram Ramachandrula, Suryaprakash Kompalli, "Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching," icfhr, pp.283-288, 2010 12th International Conference on Frontiers in Handwriting Recognition, 2010
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