loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
16th International Conference on Pattern Recognition (ICPR'02) - Volume 2
Word Segmentation of Printed Text Lines Based on Gap Clustering and Special Symbol Detection
Quebec City, QC, Canada
August 11-August 15
ISBN: 0-7695-1695-X
ASCII Text
x 
 
Soo H. Kim, Chang B. Jeong, Hee K. Kwag, Ching Y. Suen, "Word Segmentation of Printed Text Lines Based on Gap Clustering and Special Symbol Detection," Pattern Recognition, International Conference on, vol. 2, pp. 20320, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 2, 2002.
 
 
BibTex
x
 
@article{ 10.1109/ICPR.2002.1048304,
author = {Soo H. Kim and Chang B. Jeong and Hee K. Kwag and Ching Y. Suen},
title = {Word Segmentation of Printed Text Lines Based on Gap Clustering and Special Symbol Detection},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2002},
issn = {1051-4651},
pages = {20320},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.1048304},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Word Segmentation of Printed Text Lines Based on Gap Clustering and Special Symbol Detection
SN - 1051-4651
SP
EP
A1 - Soo H. Kim,
A1 - Chang B. Jeong,
A1 - Hee K. Kwag,
A1 - Ching Y. Suen,
PY - 2002
KW - null
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
Soo H. Kim, Chonnam National University
Chang B. Jeong, Chonnam National University
Hee K. Kwag, Korean Advanced Institute of Science and Technology
Ching Y. Suen, Concordia University
This paper proposes a word segmentation method for machine-printed text lines. It utilizes gaps and special symbols as delimiters between words. A gap clustering technique is used to identify the gaps between words regardless of the gap-size variations among different document images. Next a special symbol detection technique is applied to find two types of special symbols lying between words. An experiment with 1,675 text lines in 100 different English and Korean documents shows that the proposed method achieves a high accuracy of word segmentation.
Citation:
Soo H. Kim, Chang B. Jeong, Hee K. Kwag, Ching Y. Suen, "Word Segmentation of Printed Text Lines Based on Gap Clustering and Special Symbol Detection," icpr, vol. 2, pp.20320, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 2, 2002
Usage of this product signifies your acceptance of the Terms of Use.