Smart Archives: A Machine Learning Approach

Abstract

Digital archival data have an increasing exponential capacity, thereby revealing major weaknesses of conventional archival systems such as sluggish retrieval speed, erroneous categorization, and poor adaptation to heterogeneous formats. While the avenue that machine learning (ML) presents is highly promising for transformation, its exploration within the domain of archival science remains limited, with specific emphasis on multimodal data. This study outlines a hybrid ML framework to bring together CNN-based image document classification with transformer-based natural language processing for metadata extraction. With three datasets—historical manuscripts, government records, and multimedia archives—advanced data augmentation, optical character recognition (OCR) correction, and multimodal fusion were applied. Evaluation was based on precision, recall, F1-score, and latency metrics, along with user surveys. The framework achieved a mean F1-score of 0.92, outperforming rule-based systems that scored an F1 of 0.76. Image classification results of 94% on degraded manuscripts and 40% on lessening errors in metadata by NLP. User testing timed 60% faster retrievals, and 82% of archivists rated the system “intuitive” or “highly efficient.” ML enhances archival efficiency, scalability, and accuracy in a significant way. The study offers a validated roadmap for implementing ML in a range of archival setups, addressing key challenges in digital preservation. We did all experiments on simulated generated datasets due to hardware limitations and thus provide a proof of concept for the development of scalable ML archival frameworks.