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.
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Soumya, A., Kumar, G.H. (2016). Fourier Features for the Recognition of Ancient Kannada Text. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_42
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DOI: https://doi.org/10.1007/978-81-322-2734-2_42
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