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Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition

by Bhagirath Kumar, Niraj Kumar, Charulata Palai, Pradeep Kumar Jena, Subhagata Chattopadhyay
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 3
Year of Publication: 2013
Authors: Bhagirath Kumar, Niraj Kumar, Charulata Palai, Pradeep Kumar Jena, Subhagata Chattopadhyay
10.5120/9908-4500

Bhagirath Kumar, Niraj Kumar, Charulata Palai, Pradeep Kumar Jena, Subhagata Chattopadhyay . Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition. International Journal of Computer Applications. 61, 3 ( January 2013), 17-22. DOI=10.5120/9908-4500

@article{ 10.5120/9908-4500,
author = { Bhagirath Kumar, Niraj Kumar, Charulata Palai, Pradeep Kumar Jena, Subhagata Chattopadhyay },
title = { Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 3 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number3/9908-4500/ },
doi = { 10.5120/9908-4500 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:05.819667+05:30
%A Bhagirath Kumar
%A Niraj Kumar
%A Charulata Palai
%A Pradeep Kumar Jena
%A Subhagata Chattopadhyay
%T Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 3
%P 17-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Optical Character Recognition (OCR) is one of the challenging areas in the domain of image processing, where the handwritten or printed characters are digitized by using an optical scanner. The image is then analyzed broadly by two methods – (i) matrix space analysis method and (ii) feature space analysis method. Matrix space analysis method takes more memory space and time, compared to feature space analysis. However, it works fine for the scripts in which the strokes are prominent, e. g. English numeric scripts. On the other hand, the feature analysis method is useful where the scripts are complex and having more similarity between the letters in its writing style. Hence, the feature analysis approach is more useful to many of the regional languages. In this paper, we have used the Ant-miner algorithm (AMA) for offline OCR of hand written Oriya scripts, popularly known as Utkal lipi. The AMA is a rule-based approach. The rules are incrementally tuned during the training. The Oriya language contains more than 50 distinct characters i. e. 12 Swara-varnas (i. e. , vowels) and 38 Byanjan-varnas (i. e. , consonants) and their composite characters. In this work, for the analysis, we define three types of 'block's as per the writing styles of the scripts. AMA is then tested with four characters from each 'block'. Finally, a character recognition tool has been developed using Matlab for observation and validation.

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Index Terms

Computer Science
Information Sciences

Keywords

Off-line character recognition Optcal character recgnition image enhancemen Oriya scripts Ant-minor algorithm Feature space analysis

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