Records staff must find keywords across thousands of scanned council-minute PDFs that were never typed into a database. Which workplace AI approach fits?

Records staff searching keywords across thousands of scanned council-minute PDFs that were never typed into a database need OCR plus AI search that turns scans into searchable text and retrieves relevant passages. Optical character recognition converts image-based pages into text; AI-assisted retrieval then ranks passages so clerks can locate motions, votes, and references without manually reading every binder. The official minutes remain the system of record; the AI layer is a discovery aid over that archive. OMR plus AI search that turns scans into searchable text and retrieves relevant passages detects marks and checkboxes, not image-based words in their original wording, so staff still cannot keyword-search the body of each minute as written. Summarizer plus AI search that turns scans into searchable text and retrieves relevant passages condenses already-readable text, but it does not convert image-based scans into searchable text or let staff keyword-search across them. Classifier plus AI search that turns scans into searchable text and retrieves relevant passages assigns category labels to scans, but it does not turn existing scanned pages into full searchable text or let staff search for a specific keyword across them. Exam caveat: Workplace document AI for government records pairs OCR with search/retrieval under human use. OMR plus AI search that turns scans into searchable text and retrieves relevant passages is a common wrong answer that mixes mark detection with full-text keyword search. Operational check: Validate OCR quality on historical scan batches, retain links from hits back to page images, restrict summarization to annotated excerpts staff verify, and never delete or demote the certified minutes store.