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Reseach Article

Statistical Approach for Segmenting Unconstrained Handwritten Text lines

Published on January 2013 by Gomathi Rohini. S, Umadevi. R. S, Mohanavel. S
Amrita International Conference of Women in Computing - 2013
Foundation of Computer Science USA
AICWIC - Number 1
January 2013
Authors: Gomathi Rohini. S, Umadevi. R. S, Mohanavel. S
bfa3ceea-ba1a-420b-bf00-9aad108d44e9

Gomathi Rohini. S, Umadevi. R. S, Mohanavel. S . Statistical Approach for Segmenting Unconstrained Handwritten Text lines. Amrita International Conference of Women in Computing - 2013. AICWIC, 1 (January 2013), 20-24.

@article{
author = { Gomathi Rohini. S, Umadevi. R. S, Mohanavel. S },
title = { Statistical Approach for Segmenting Unconstrained Handwritten Text lines },
journal = { Amrita International Conference of Women in Computing - 2013 },
issue_date = { January 2013 },
volume = { AICWIC },
number = { 1 },
month = { January },
year = { 2013 },
issn = 0975-8887,
pages = { 20-24 },
numpages = 5,
url = { /proceedings/aicwic/number1/9862-1304/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Amrita International Conference of Women in Computing - 2013
%A Gomathi Rohini. S
%A Umadevi. R. S
%A Mohanavel. S
%T Statistical Approach for Segmenting Unconstrained Handwritten Text lines
%J Amrita International Conference of Women in Computing - 2013
%@ 0975-8887
%V AICWIC
%N 1
%P 20-24
%D 2013
%I International Journal of Computer Applications
Abstract

The segmentation of unconstrained handwritten text lines into words is an important stage in word recognition systems. This paper addresses a methodology to overcome the challenges, which are amplified by the non-uniform spaces between words and overlapping components by using a few statistical approaches. The system was developed using Java 2 and ImageJ tool. In this approach, a text line image is scanned vertically, holding only the spatial information. A scheme based on distance metrics and gap classification into inter-word gap and intra-word gap is presented. The threshold value is determined by using arithmetic mean, inter-quartile mean or trimmed mean based on the variation in the text. A pre-processing of removal of noise and correction of skew angle and dominant slant angle were done to improve the recognition accuracy. The system was illustrated with a few cases. A quantitative analysis of the experiment done on the system by using 1100 text lines from IAM database achieved an accuracy of 96. 72% and found the system faster and reliable. Further, the proposed method is compared with the contour based and non-contour based techniques.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Inter-quartile Mean Projection Profile Connected Component Distance Metrics