Recognition and Detection of Language on Inscriptions

Dr. C Parthasarathy, R.Sarvanan, M Sathish, U.Sai Sri Teja

Ancient language Font Recognition is one of the Challenging tasks in Optical Character Recognition and Document Analysis. Most of the existing methods are for font recognition make use of local typographical features and connected component analysis. In this paper, Ancient language font recognition is done based on global texture analysis. Ancient language characters are different from current  century’s Ancient language character. This paper concentrates on the century identification of ancient language characters and converting them into current century’s form using MATLAB. Recognition of ancient language hand written characters from inscriptions is difficult. In this paper, a method for recognizing Ancient language characters from stone inscriptions, called the contour-let transform, which has been recently introduced, is adopted. From the previous research works, it’s noticed that Wavelet transforms are not capable of reconstructing curved images are perfectly. The contour-let transform offers a solution to remedy to this insufficiency. Contour-let transform is a 3D approach technique where as wavelet transform is a 2D technique. The characters from the input image are recognized through the clustering mechanism. Further the noise is present in the image is removed by fuzzy median filters. Neural networks are been employed to train the image and compare the data with the current century’s character. hence a more accurate recognition of Ancient language characters from stone inscriptions is obtained.