Impact of Total Variation Regularization on Character Segmentation from Historical Stone Inscriptions

Abstract

An automatic segmentation scheme for accurate segmentation of characters from Historical Handwritten Kannada Stone Inscription images is presented in this paper. The accuracy of Character Segmentation plays a vital role in facilitating Optical Character Recognition from these text documents. Such an Optical Character Recognition system is a crucial part in the design of an intelligent system which can perform automatic transliteration of the old Kannada text in the inscriptions to its modern counterpart. The stone inscription documents are heavily degraded due to various factors like aging, depositions, risky handling, non uniform illumination and unclear separation between the foreground text and the background. An attempt to suppress these noise degradations might also remove useful text edges thus undermining the performance of the segmentation process. Hence an appropriate image enhancement phase is crucial for these document images. Total Variation Regularization (TVR) is an image contrast enhancement method which denoises the image by smoothing without blurring the sharp edges. A framework of TVR enhancement and Connected Component Labeling segmentation was implemented and evaluated on the dataset of digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI). These Estampages are preserved at the Archeological Survey of India. A global contrast enhancement factor of 1.1528 and character segmentation accuracy of 79.2% was achieved on this dataset. Experimentation validated the improvements over previously reported state of the art methods.