olmOCR-2 as the Canonical PDF Ingestion Engine

@techreport{hamilton2026olmocr, author = {Hamilton, Michael P., Ph.D.}, title = {olmOCR-2 as the Canonical PDF Ingestion Engine}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-TN-2026-049}, month = {april}, url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-049}, abstract = {The first-light OCR evaluation on April 10, 2026 (see REPORT.md in `tests/ocr\_bench/`) compared two text extraction pipelines against four PDFs: PyMuPDF `fitz` for text-layer extraction and `glm-ocr` via local Ollama for vision-model OCR. That study recommended a standalone `ocr\_document` worker wrapping glm-ocr as the Phase 1 build for the Collaboratory document pipeline. Before committing to that build, an evening follow-up benchmark was run against a third candidate: allenai olmOCR-2, the flagship OCR model from the Allen Institute, in the `mlx-community/olmOCR-2-7B-1025-5bit` port running natively on Apple Silicon through `mlx\_vlm.server` and the `olmocr` CLI. The follow-up benchmark added a second, harder corpus of three PDFs: a theconversation.com science article, a nine-page 2017 Elsevier review on microbial mat ecosystems with 101 references and dense chemistry notation, and a twenty-four-page OSU Extension gardening guide with a forty-five-row regional planting calendar. olmOCR-2 delivered clean academic apparatus (bibliographic headers, ToC with original pagination, hierarchical section numbering, inline citations, full references with DOI URLs, Unicode subscripts in chemistry notation), reconstructed the centerpiece forty-five-row by ten-column planting calendar as structured HTML, and correctly classified decorative stock photography as non-semantic (stripping the images while retaining their text attribution credits). Steady-state throughput held at approximately 28.7 seconds per page for text-dense content and 5.6 seconds per page for sparse slide decks. Zero failures across 37 pages in a single batch invocation. The v0.2 recommendation is superseded. **olmOCR-2 is the canonical PDF-to-markdown path for the Collaboratory document pipeline.** Phase 1 is retargeted from a glm-ocr wrapper to a `read\_document\_olmocr` worker. Phase 2 (hybrid fitz-plus-glm-ocr orchestration with selective trigger logic) is no longer needed; olmOCR-2 subsumes both pipelines. This note documents the benchmark methodology, the evidence supporting the decision, the known caveats that remain open, and the forward work required to ship olmOCR-2 as a production worker.} }