Just-in-Time Agentic OCR

Just-in-time agentic OCR is presented as a two-pass document-processing approach for ad-hoc data rooms of roughly 10–100 files: agents first perform inexpensive text extraction across all uploads to enable keyword or semantic retrieval, then apply more accurate but costly vision-language-model OCR only to the pages identified as relevant. An example using 84 SEC filings shows how this method can isolate a few pages containing difficult financial tables rather than processing all 12,013 pages with VLM OCR, while page-complexity detection can further identify scanned, sparse-text, image-heavy, or table-heavy pages that may need enhanced parsing. The approach is argued to work best for interactive investigations because agents can compensate for imperfect initial retrieval through repeated searches, but it is considered unsuitable for offline corpora of thousands to millions of documents, where VLM-quality parsing of every page should occur before indexing to protect retrieval accuracy. The discussion also identifies limitations in common tools such as pdftotext and general frontier models, including poor handling of scans, layouts, tables, grounding, auditability, and non-PDF formats, and promotes LiteParse as a low-cost first-pass parser and LlamaParse as a targeted VLM-based second-pass service with page selection, table structures, bounding boxes, confidence scores, and optional background processing.

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