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

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 3
Year of Publication: 2013
Bhagirath Kumar
Niraj Kumar
Charulata Palai
Pradeep Kumar Jena
Subhagata Chattopadhyay

Bhagirath Kumar, Niraj Kumar, Charulata Palai, Pradeep Kumar Jena and Subhagata Chattopadhyay. Article: Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition. International Journal of Computer Applications 61(3):17-22, January 2013. Full text available. BibTeX

	author = {Bhagirath Kumar and Niraj Kumar and Charulata Palai and Pradeep Kumar Jena and Subhagata Chattopadhyay},
	title = {Article: Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {3},
	pages = {17-22},
	month = {January},
	note = {Full text available}


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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