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Fourier Features for the Recognition of Ancient Kannada Text

  • A. SoumyaEmail author
  • G. Hemantha Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)

Abstract

Optical Character Recognition (OCR) System for ancient epigraphs helps in understanding the past glory. The system designed here, takes a scanned image of Kannada epigraph as its input, which is preprocessed and segmented to obtain noise-free characters. Fourier features are extracted for the characters and used as the feature vectors for classification. The SVM, ANN, k-NN, Naive Bayes (NB) classifiers are trained with different instances of ancient Kannada characters of Ashoka and Hoysala period. Finally, OCR system is tested on epigraphical characters of 250 from Ashoka and 200 from Hoysala period. The prediction analysis of SVM, ANN, k-NN and NB classifiers is made using performance metrics such as Accuracy, Precision, Recall, and Specificity.

Keywords

Fourier descriptors Support Vector Machine (SVM) Artificial Neural Network (ANN) k-Nearest Neighbor (k-NN) Naive Bayes (NB) classifier 

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringRV College of EngineeringBengaloreIndia
  2. 2.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia

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