Recognition and Classification of Stone Inscription Character Using Artificial Neural Network K. Durga Devi and P. Uma Maheswari 1 Introduction Stone inscriptions have been found in substantial numbers, holding huge volumes of authentic information from the ancient to the medieval period, beginning from 3rd millennium BCE. Presently, epigraphical efforts are focused on locating places where inscriptions are found, labor-intensive copying called estampaging, and the manual transliteration and scanning of estampage that is indecipherable. Further, the man power required for transliteration is very scarce, leading to a great loss of information. Durga and Maheswari [1] presented a survey about optical character recognition (OCR) system that has been developed for handwritten and scanned document images for international languages like Arabic, Chinese, Thai, and English, as well as for national languages like Hoysala, Bangla, Marathi, Telugu, and Tamil. However, very few studies have been carried out on stone inscription images. In today’s fast-moving world with its incredible technology, character recognition is a demanding area of study. In recent decades, the most demanding research area has been the design of an OCR system. K. Durga Devi (&) Department of ECE, SRMIST Ramapuram, Chennai, India P. Uma Maheswari Department of CSE, CEG, Anna University, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 R. R. Raje et al. (eds.), Artificial Intelligence and Technologies, Lecture Notes in Electrical Engineering 806, https://doi.org/10.1007/978-981-16-6448-9_26 253