Deep Learning-powered Mobile App for Early Brahmi Script Decipherment in Sri Lanka

This ongoing research delves into ancient Brahmi writings carved on stone surfaces, particularly focusing on early Brahmi characters which serves as critical artifacts to illuminate the island’s historical ties with India, religious and cultural practices of the era. The study aims to develop a mobile application utilizing deep learning techniques to recognize and translate ancient early Brahmi characters, ensuring enhanced efficiency and accuracy in the decipherment process. A comprehensive literature review highlighted the absence of a mobile application, challenges of contextual translation, and difficulties in accurate predicting translation for damaged inscriptions and translation to foreign languages were identified as notable research gaps. Drawing upon interdisciplinary expertise and stakeholder analysis, the research tackles these complex challenges of script recognition, linguistic translation, accurate prediction by employing a data-driven approach, cutting-edge algorithms, and user-centric design principles. After the creation of a refined, precise digitized dataset of early Brahmi characters with found variations, the research aims to utilize semantic segmentation techniques to recognize characters using TensorFlow and Keras. OpenCV for image preprocessing, Flutter framework for development of mobile application. Expected outcomes include improved recognition accuracy, linguistically faithful translations, and user-friendly interfaces, contributing to advancements in digital humanities, cultural preservation, and computational linguistics.