Preprocessing and Binarization of Inscription Images using Phase Based Features

Epigraphs are important sources for reshaping our culture and history. They have a remarkable importance to mankind. But modern epigraphists find it difficult to interpret the information in scripts. It is mainly because inscriptions are eroded over a period of time due to natural calamities. Scripts of ancient times are largely unknown. Character sets used have changed from one form to another over the centuries. Therefore, for reading ancient scripts the characters have to be extracted. In this paper, a model for enhancement and binarization of historical epigraphs is proposed. This model consists phase congruency and of Gaussian model based background elimination using expectation maximization(EM) algorithm, preprocessing and binarization. In binarization, phase based features are used with specialised filters. Adaptive Gaussian filters are used to smoothen the output images. Weighted mean angle is calculated to differentiate the foreground from the background. EM algorithm removes the background noise completely where foreground characters are untouched. Proposed method is tested on different datasets of inscriptions and epigraphs. Obtained results are compared with the existing classical algorithms.