NeoLipi: A Multi-Script Ancient Inscription Recognition and Translation System Using Deep Learning

Abstract: Ancient stone inscriptions contain valuable information about the history, culture, language, religion and ad- ministration of earlier periods. However, deciphering such inscriptions are difficult because the characters may be faint, broken, damaged or mixed with the texture of the stone. This paper presents NeoLipi, a multi-script ancient inscription recognition and Kannada translation system developed for Brahmi, Tamil(Vatteluttu) and Devanagari inscriptions. The system uses different recognition methods for the three script groups. The Brahmi pipeline performs image enhancement, binarization, line and character segmentation, stroke reconstruction and CNN-based character recognition. The Tamil pipeline performs binarization, stroke refinement, line seg-mentation, character extraction and CNN-based recognition. The Devanagari pipeline uses grayscale conversion, bilateral denoising, CLAHE, background normalization and an OCR-based recognition module. The recognized text is converted into digital text using Unicode mapping where required and is then translated into Kannada. The system is implemented using Python, OpenCV, NumPy, SciPy, TensorFlow/Keras and Streamlit. The proposed system provides a simple way to process historical inscription images and supports their digital preservation.

Keywords: Ancient inscriptions, Brahmi, Tamil, Devana-gari, CNN, OCR, image processing, Unicode, Kannada translation.