Ancient Text Modernization Using Deep Learning

Abstract

Ancient Indian manuscripts, written in classical languages such as Sanskrit, Pali, Prakrit, and Tamil, form the cornerstone of India’s vast intellectual heritage. These texts encapsulate centuries of wisdom in diverse domains such as astronomy, Ayurveda, mathematics, linguistics, and philosophy. However, the accessibility and comprehension of the set exts have declined due to linguistic evolution, material degradation, and limited expertise in ancient grammar and semantics. Traditional manual translation and preservation methods are labor-intensive, error-prone, and insufficient to meet the demands of large-scale digitization. This study introduces a Deep Learning (DL)-based framework for Ancient Text Modernization aimed at bridging the temporal and linguistic gap between ancient and modern readers. The proposed model integrates Optical Character Recognition (OCR), Convolutional Neural Networks (CNNs) for character recognition, Recurrent Neural Networks (RNNs) for sequence learning, and Transformer architectures for context-aware modernization and translation. The frame work automates the end-to-end process of text extraction, transliteration, syntactic reformation, semantic interpretation, and contextual adaptation of ancient texts into modern readable formats. By fine-tuning pre-trained multilingual models such as Indic BERT and leveraging large parallel corpora from Sanskrit, Tamil, and Pali sources, the system ensures linguistic accuracy while preserving cultural nuances. The results demonstrate significant improvement in translation coherence, semantic cretention, and contextual readability compared to traditional rule-based systems. The modernization process not only preserves heritage but also empowers researchers, linguists, and students to explore ancient knowledge through modern interfaces. This approach contributes to digital humanities and computational Indology by offering a sustainable AI-driven pathway for reviving and modernizing ancient Indian knowledge systems.