A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition

Tusar Kanti Mishra, Banshidhar Majhi and Ratnakar Dash
Volume: 13, No: 1, Page: 174 ~ 183, Year: 2017
10.3745/JIPS.02.0012
Keywords: Contour Features, Handwritten Character, Neural Classifier, Numeral Recognition, OCR, Odia
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Abstract
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 (?), and arc-to- chord 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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Cite this article
IEEE Style
Tusar Kanti Mishra, Banshidhar Majhi, and Ratnakar Dash, "A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition ," Journal of Information Processing Systems, vol. 13, no. 1, pp. 174~183, 2017. DOI: 10.3745/JIPS.02.0012.

ACM Style
Tusar Kanti Mishra, Banshidhar Majhi, and Ratnakar Dash, "A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition ," Journal of Information Processing Systems, 13, 1, (2017), 174~183. DOI: 10.3745/JIPS.02.0012.