AI-Powered System for Efficient Text Search in Scanned Manuscripts

The digitization of historical manuscripts has facilitated their preservation and global accessibility. However, searching for specific phrases or words within scanned images of these manuscripts remains a significant challenge due to diverse handwriting styles, faded ink, and non-standard layouts. This research presents an AI-powered system that enables efficient and accurate text search in scanned manuscripts by leveraging Optical Character Recognition (OCR), deep learning, and Natural Language Processing (NLP) techniques. The system incorporates advanced image pre-processing, text detection, feature extraction, and indexing to enhance search accuracy and efficiency. The proposed model achieves an average text recognition accuracy of approximately 75% for well-preserved documents and 60% for moderately degraded manuscripts. By improving recognition accuracy, the proposed model aims to assist researchers, historians, and archivists in retrieving relevant information from vast collections of historical documents, ultimately aiding in cultural and historical preservation