Abstract
Handwritten recognition is a challenging task in the area of pattern recognition and machine learning. Challenges arise due to variability observed in style, shape, and structure associated with individual writings. In this paper, we have made an attempt to compare four different feature extractions cum classifier schemes for handwritten Odia numeral recognition on the basis of recognition accuracy and computational time. It is found that single decision tree is better in terms of computational time whereas Support Vector Machine (SVM) is better in terms of recognition accuracy.
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Behera, S., Das, N. (2020). Handwritten Odia Numerals Recognition: A Supervised Learning Perspective. In: Mohanty, M., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2774-6_11
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DOI: https://doi.org/10.1007/978-981-15-2774-6_11
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