Non-Redundant Contour Directional Feature Vectors for Character Recognition

Authors

  • Tusar Kanti Mishra GITAM University image/svg+xml
  • Sandeep Panda Goldman Sachs
  • Banshidhar Majhi Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram image/svg+xml

DOI:

https://doi.org/10.4108/eai.19-11-2020.167204

Keywords:

OCR, Odia character recognition, pattern recognition, classification, feature extraction

Abstract

This paper presents a novel shape based feature for printed character recognition. The shape features are derived from the contour of the character which is unique to all characters. Preprocessing is performed to standardize the characters and handle all variations such as bold, italics and bold-italics font characteristics. The complete character set is clustered into different groups based on contour feature. A probe character is mapped into the corresponding cluster prior to recognition. This helps to reduce the computational overhead. Finally two recognition schemes have been proposed, based on angle feature extracted from the contour information and a longest common substring (LCS) based feature. Simulation has been carried out to validate the efficacy of the proposed scheme on printed Odia characters. Performance accuracy has been compared with the existing schemes. In general, it is observed that the proposed scheme outperforms the competitive schemes.

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Published

19-11-2020

How to Cite

1.
Kanti Mishra T, Panda S, Majhi B. Non-Redundant Contour Directional Feature Vectors for Character Recognition. EAI Endorsed Trans Creat Tech [Internet]. 2020 Nov. 19 [cited 2024 Apr. 18];7(25):e3. Available from: https://publications.eai.eu/index.php/ct/article/view/1436