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Script Identification using Discrete Curvelet Transforms

Published on February 2014 by B. V. Dhandra, Vijayalaxmi. M. B, Gururaj Mukarambi, Mallikarjun Hangarge
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National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 4
February 2014
Authors: B. V. Dhandra, Vijayalaxmi. M. B, Gururaj Mukarambi, Mallikarjun Hangarge
629d045f-1ec3-47ca-88f8-599a5bbd7653

B. V. Dhandra, Vijayalaxmi. M. B, Gururaj Mukarambi, Mallikarjun Hangarge . Script Identification using Discrete Curvelet Transforms. National Conference on Recent Advances in Information Technology. NCRAIT, 4 (February 2014), 16-20.

@article{
author = { B. V. Dhandra, Vijayalaxmi. M. B, Gururaj Mukarambi, Mallikarjun Hangarge },
title = { Script Identification using Discrete Curvelet Transforms },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 4 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/ncrait/number4/15163-1432/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A B. V. Dhandra
%A Vijayalaxmi. M. B
%A Gururaj Mukarambi
%A Mallikarjun Hangarge
%T Script Identification using Discrete Curvelet Transforms
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 4
%P 16-20
%D 2014
%I International Journal of Computer Applications
Abstract

This paper presents discrete curvelet transform (DCvT) based block level handwritten script identification. The conventional two-dimensional (2-D) discrete wavelet transforms (DWTs), de-emphasizes directional discriminating properties such as curves, lines and edges of the texture under study and whereas discrete curvelet transform (DCvT) efficiently extracts directional selective features. Typically it can be observed that the patterns of any handwritten text blocks encompass directionally dominant texture primitives. Therefore, the primary aim of this paper is to show the efficiency of discrete curvelet transform (DCvT) in describing the handwritten text blocks of six Indian scripts. Exhaustive experimentations were conducted on a large dataset with various combinations of scripts. For instance, average script classification accuracy achieved in case of bi-scripts and tri-scripts combinations are 94. 19% and 95. 24% respectively.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Bilingual Trilingual Multilingual Script Identification Curvelet Transform Nearest Neighbor Classifier Texture Features.