Fourier Features for the Recognition of Ancient Kannada Text

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.

Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System

“…The 3D feature-based character recognition system for palm leaf manuscripts are presented in [7] with an accuracy of 96%. Soumya A. and G. Hemantha Kumar [8] presented work on Fourier feature-based classifiers for recognition of Kannada epigraphs. This method performs feature extraction in the first step, then global recognition was performed by comparing the representation of the unknown word with the references stored in the lexicon.…”

Recognition of Historical Records Using Gabor and Zonal Features

“…The intermediate components of the system consist of: Preprocessing, Segmentation, Feature extraction, Recognition and Post-processing [11,12]. The input to the system is ancient Kannada epigraphic documents.…”