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June 16, 2020
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June 16, 2020
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Abstract

Purpose: Lines and Curves are important parts of characters in any script. Features based on lines and curves go a long way to characterize an individual character as well as differentiate similar-looking characters. The present paper proposes an English numerals recognition system using feature elements obtained from the novel and efficient coding of the curves and local slopes. The purpose of this paper is to recognize English numerals efficiently to develop a reliable Optical Character recognition system.


Methodology: K-Nearest Neighbour classification technique has been implemented on a global database MNIST to get an overall recognition accuracy rate of 96.7 %, which is competitive to other reported works in literature. Distance features and slope features are extracted from pre-processed images. The feature elements from training images are used to train K-Nearest-Neighbour classifier and those from test images have been used to classify them.


Main Findings: The findings of the current paper can be used in Optical Character Recognition (OCR) of alphanumeric characters of any language, automatic reading of amount on bank cheque, address written on envelops, etc.


Implications: Due to the similarity in structures of some numerals like 2, 3, and 8, the system produces respectively lower recognition accuracy rates for them.


Novelty: The ways of finding distance and slope features to differentiate the curves in the structure of English Numerals is the novelty of this work.

Keywords

Slope Coding Curve Coding English Numerals Recognition K-Nearest Neighbour Optical Character Recognition Delta Distance Coding

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Authors retain the copyright without restrictions for their published content in this journal. IJSRTM is a SHERPA ROMEO Journal. 

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This is an open-access article distributed under the terms of  Creative Commons License

 

How to Cite
Kumar Prasad, B. (2020). SLOPE AND CURVE CODING TO RECOGNIZE ENGLISH NUMERALS. International Journal of Students’ Research in Technology & Management, 8(2), 15–20. https://doi.org/10.18510/ijsrtm.2020.823
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