vlm-ocr-pipeline
VLM-based OCR pipeline: model selection, prompts, architecture, evaluation.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill vlm-ocr-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 `reference/prompt-and-schema.md`. ### 1. Model Selection - **Start from OCR benchmarks, not general VLM leaderboards.** OCRBench (Liu et al. 2024) tests across 29
What does the vlm-ocr-pipeline skill do?
VLM-based OCR pipeline: model selection, prompts, architecture, evaluation.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill vlm-ocr-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.