This paper examines literature from the field of Computational Archival Science (CAS) to track efforts to address the challenges of the digital age in archives. The Digital Tsunami presented archives with a problem of scale, challenges with Digital Fragility, and changing modes of access which CAS sought to address with computational methods. The born-digital revolution presents the challenge of the Vanishing Box - the loss of topical and temporal structure in records created by the digital workforce leaving collections of Virtual Machines containing chaotic virtual drifts of items with limited metadata suffering from delays in digital preservation. This raised further issues of scale as well as requiring a fundamental rethinking of foundational theories of archival science which CAS sought to address with changes to the Appraisal, Records Management, Description, and Preservation practices previously developed for analog and digitized records.

This paper proposes, tests, and evaluates an innovative Computational Archival Science (CAS) framework to enhance the ability to link people, places, and events depicted in historical photography collections. The protocol combines elements of computer vision with natural language processing, entity extraction, and metadata linking techniques to transform and connect existing archival metadata. Development of the framework is built upon a case study based on the Spelman College Archives Photograph Collection and provides background information, reports on the text processing, image analysis, semantic linking, and evaluation aspects associated with the design and use of the AI-supported framework.