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- 저자 : ( Tusar Kanti Mishra ) (검색결과 2 건)
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A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition
Mishra, Tusar Kanti,Majhi, Banshidhar,Dash, Ratnakar Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.1
In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (${\theta}$), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages-Bangla and English. In general, we observed that our proposed contour features outperform other schemes.
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A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition
( Tusar Kanti Mishra ),( Banshidhar Majhi ),( Ratnakar Dash ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.1
In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (θ), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages― Bangla and English. In general, we observed that our proposed contour features outperform other schemes.
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