Handwritten Odia Numerals Recognition: A Supervised Learning Perspective

  • Suchismita BeheraEmail author
  • Niva Das
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


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.


Numeral recognition HOG SVM Single decision tree classifier 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of ECESiksha ‚‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia

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