vlm-ocr-pipelinelisted

# VLM-Based OCR Pipeline for Scanned Document Corpora ## Instructions For a worked language-specific transcription prompt (pre-reform Cyrillic) and a per-page JSON output schema with `uncertain_spans`, `layout_markers`, and `flags`, see `references/prompt-and-schema.md`. ### 1. Model Selection - **Run your own comparison before selecting — this is a gate, not a preference.** Benchmarks and published studies narrow the candidate set; they do not decide it. Score the shortlist on a human-transcribed sample of your own pages and let the measured error rates pick the model. See `$vlm-ocr-evaluation` for the comparison protocol, the stratified ground-truth design, and the go/no-go gate. Everything below narrows the shortlist that comparison scores. - **Start from OCR benchmarks, not general VLM leaderboards.** OCRBench (Liu et al. 2024) tests across 29 document OCR dimensions; OCRBench v2 (Fu et al. 2025) extends to multilingual scripts and multi-page documents. General vision-language benchmarks (MMMU, VQAv2) do not predict OCR accuracy. - **Verify language support explicitly.** Confirm the target language appears in the model's training set with per-language accuracy data. Qwen3-VL (Bai et al. 2025) enumerates 39 languages with OCR benchmarks; models that claim "multilingual support" without language-specific evidence may fail on non-Latin scripts. - **Compare across models for your script family.** E-ARMOR (2025) compares five VLMs and two traditional OCR systems across 54