Arolsen Archives

Solution

Accenture and Arolsen Archives built an AI-assisted document-processing workflow to speed the indexing of Holocaust-related records while keeping historians and volunteers in the loop. The solution uses cloud-based technologies, optical character recognition, and AI and machine learning techniques to analyze archive documents ranging from prisoner lists and transfer records to tracing requests submitted by families. Each extracted field receives a confidence score so clearly readable documents can be processed quickly while lower-confidence cases are routed for human validation. Feedback from volunteers and historians is then fed back into the system so the models learn how to interpret difficult handwriting, degraded documents, and inconsistent formats over time. The workflow also clusters similar documents for easier review and replaces a slow, four-person validation pattern with a collaborative human-plus-machine process.

Data flow

Archive documents are digitized and read through OCR, then processed by AI and machine learning models that extract fields such as names and assign confidence scores. High-confidence outputs move forward quickly, while lower-confidence cases are reviewed by volunteers and historians whose feedback is fed back into the system so later documents can be interpreted more accurately and clustered for faster review.