Transforming Physical Archives into Searchable Digital Libraries with Optical Character Recognition

Abstract This study presents an integrated and extensible OCR framework that transforms physical documents into searchable digital archives, emphasizing system-level engineering over algorithmic novelty. While the core components such as contrast enhancement, PaddleOCR, and BERT-based spelling correction are individually established, their synthesis into a unified pipeline for multilingual and multimodal archival digitization represents a practical contribution to digital preservation. The architecture is implemented using Python and incorporates preprocessing steps such as grayscale conversion, CLAHE-based contrast adjustment, Gaussian blurring, and adaptive thresholding to enhance image quality. Multilingual high-accuracy text recognition is performed using PaddleOCR, while a hybrid spelling correction module combines lexical and BERT-based contextual corrections. A straightforward and easy-to-use graphical user interface (GUI) facilitates interaction and visual diagnostics on the basis of confidence heatmaps as well as pixel intensity histograms. Digitized outputs are retained in a central library repository to facilitate efficient retrieval and organization. The system performs well in high OCR accuracy across different input types 99.12 % in screenshots and 94.81 % in scanned documents. Preprocessing greatly enhances text readability in degraded images. The spelling correction module enhances text readability by more than 95 %. Visualization tools offer useful insights into OCR performance and preprocessing effects. This paper demonstrates a modular pipeline that integrates preprocessing, OCR, error correction, visualization, and repository integration. The application of PaddleOCR with domain-specific preprocessing and contextual correction capabilities brings novelty, particularly for rich archival content.