Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network

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virendra nikam
virendra nikam
2
Dr. Archana P.Jane
Dr. Archana P.Jane
3
Prof.Mukesh.A.Pund
Prof.Mukesh.A.Pund
1 Prof.Ram Meghe Institute of Research & Technology, Badnera .

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The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters. Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

virendra nikam. 2012. \u201cRecognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 12 (GJCST Volume 12 Issue D11): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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September 15, 2012

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English

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The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters. Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

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Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network

Dr. Archana P.Jane
Dr. Archana P.Jane
Prof.Mukesh.A.Pund
Prof.Mukesh.A.Pund

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