Digital Transformation of Indian Archives and Epigraphy: The Role of OCR and HTR

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in historical research and archival studies, reshaping traditional methodologies of data analysis, interpretation, and preservation. The rapid digitization of historical sources such as manuscripts, inscriptions, administrative records, and visual materials has created vast datasets that require advanced computational tools for effective analysis. AI technologies, including machine learning, Optical Character Recognition (OCR), and Handwritten Text Recognition (HTR), enable historians and archivists to process, classify, and interpret these sources with greater accuracy and efficiency. In archival studies, AI facilitates automated cataloguing, metadata generation, enhanced retrieval systems, and digital preservation of fragile materials. Furthermore, AI supports interdisciplinary research by integrating historical inquiry with digital humanities and data science. However, the application of AI also raises critical challenges related to algorithmic bias, contextual misinterpretation, ethical concerns, and data authenticity. This study examines the role, potential, and limitations of Artificial Intelligence in historical research and archival practices, arguing that AI, when used responsibly, serves as a powerful complementary tool that enhances scholarly rigor while preserving human interpretative authority in understanding the past.