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Reseach Article

Article:Handwritten Document Retrieval System for Tamil Language

by AN. Sigappi, S. Palanivel, V. Ramalingam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 4
Year of Publication: 2011
Authors: AN. Sigappi, S. Palanivel, V. Ramalingam
10.5120/3814-5268

AN. Sigappi, S. Palanivel, V. Ramalingam . Article:Handwritten Document Retrieval System for Tamil Language. International Journal of Computer Applications. 31, 4 ( October 2011), 42-47. DOI=10.5120/3814-5268

@article{ 10.5120/3814-5268,
author = { AN. Sigappi, S. Palanivel, V. Ramalingam },
title = { Article:Handwritten Document Retrieval System for Tamil Language },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 4 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number4/3814-5268/ },
doi = { 10.5120/3814-5268 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:17.346366+05:30
%A AN. Sigappi
%A S. Palanivel
%A V. Ramalingam
%T Article:Handwritten Document Retrieval System for Tamil Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 4
%P 42-47
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper attempts to create a handwritten document retrieval system suitable for Tamil language, with a view to record traditional literature content for future reference. It projects a search mechanism to access the query word images using a statistical model based methodology. The scheme revolves around a well defined procedure which results in word models from where the search word can be recognised and the relevant documents retrieved. The approach involves the use of hidden Markov models (HMM) to characterize the features of the dynamically varying strokes of handwritten characters. The strategy is investigated for a sample document set over a commonly used literature. The results reveal that the system yields a reasonable performance with considerable accuracy. The highlight of this procedure is that it can effectively segment differently written words from text lines in a document and imbibes in it a flexibility to cover a wide range of tilts in the strokes that are attached to the different words.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Handwritten document retrieval Profile features Segmentation Hidden Markov models