Vision-OCR

When to load

• /grade needs to convert answers/*.pdf → answers/converted/*.md
• Any hand-written / scanned document whose previous tesseract pass was garbled
• answer-processing skill's step-2 conversion

Engine choice

.course-meta holds a single line OCR_ENGINE: <engine> written by /paideia:init-course. The grade command reads it and dispatches. Users can override per-call with /paideia:grade --ocr=<engine> [path].

All three emit answers/converted/<stem>.md with a <!-- SOURCE: ... --> / <!-- TIER: ... --> header comment that lets /grade caveat the confidence.

Tier 0 — Claude native vision (default)

Pipeline (driven by the /grade command, not this script):

answers/<stem>.pdf
  ↓ pdftoppm -r 200 -png <pdf> <tmpdir>/page   # rasterize to PNG per page
  ↓ Claude reads <tmpdir>/page-1.png, page-2.png, ... via the Read tool
  ↓ Claude synthesizes clean MD following the prompt contract below
answers/converted/<stem>.md
   └── header:  <!-- SOURCE: <stem>.pdf, claude-vision (native), N pages -->

The grade command handles the orchestration — rasterize, Read each page, synthesize into one markdown file in a single pass. No standalone driver script is required.

Tier 1 — Ollama Qwen3-VL 8B (opt-in)

answers/<stem>.pdf
  ↓ pdf2image @ 300dpi
  ↓ resize to ≤1200px wide (VLMs dislike huge inputs)
  ↓ base64 JPEG per page
  ↓ [Tier 1a] ollama qwen3-vl:8b
  ↓    (on timeout / ollama down)
  ↓ [Tier 1b] pytesseract (eng or eng+kor, from .course-meta INTERFACE_LANG)  ← auto-fallback inside the same script
answers/converted/<stem>.md
   └── header:  <!-- SOURCE: <stem>.pdf, qwen3-vl:8b @ 300dpi, N pages -->
                <!-- TIER: tesseract fallback -->  (only when 1a bombed)

Entrypoint:

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/vision_ocr.py" --engine=ollama <input.pdf> <output.md>

${CLAUDE_PLUGIN_ROOT}/scripts/vision_ocr.py is the single source of truth. It:

1. Warms up the VLM with a 1-token /api/generate so the first real page isn't stalled by model load.
2. Sends each page as a JPEG-encoded base64 image with a language-aware, LaTeX-first prompt (prose-language rule selected by INTERFACE_LANG).
3. Sets keep_alive: "15m" so the model stays in memory across pages within a session.
4. On any exception (timeout, connection refused) falls back to pytesseract and marks the file.

Tier 2 — Tesseract explicit

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/vision_ocr.py" --engine=tesseract <input.pdf> <output.md>

Skips ollama entirely. Header: <!-- TIER: tesseract (explicit) -->.

Prompt contract (must preserve across Tier 0 + Tier 1)

Whether synthesized by Claude inline (Tier 0) or by Qwen3-VL through this script (Tier 1), the transcription prompt must:

• Keep prose in its original language (English, Korean, etc.) — do not translate
• Emit math as $...$ / $$...$$
• Preserve problem numbering (P1, (1), (a), ...)
• NOT grade or interpret — just transcribe
• Write [?] for ambiguous glyphs instead of guessing
• Skip crossed-out work
• Return markdown only, no <think>, no commentary

If you edit the prompt, keep these six clauses — they're what separates useful transcription from hallucination.

Dependencies

All engines need:

• poppler binaries (pdftoppm, used by pdf2image). brew install poppler / apt-get install poppler-utils.

Tier 0 (claude): nothing beyond Claude Code itself.

Tier 1 (ollama) extras:

• ollama CLI + model qwen3-vl:8b (~6.1 GB). brew install ollama && ollama serve & && ollama pull qwen3-vl:8b.
• Python: pdf2image, pytesseract, pillow.

Tier 2 (tesseract) extras:

• tesseract + tesseract-lang (or tesseract-ocr-kor on Debian). Python: pdf2image, pytesseract, pillow.

Performance notes

• Tier 0 (claude): depends on Claude's per-image processing; typically a few seconds per page with no model-load stall.
• Tier 1 (ollama) on Mac M-series: first page ~2–5 min (model load + decode); subsequent pages ~20–60 s.
• A 2-page hand-written answer typically takes 3–7 min total on Tier 1, a few seconds on Tier 0, and <10 s on Tier 2 (but fidelity falls off a cliff).
• 300dpi input is downscaled to 1200px before encoding — keeps the base64 payload under ~500 KB on Tier 1.

Failure modes + fixes

Anti-patterns

• ❌ Don't pass base64 via curl -d <arg> — ARG_MAX overflow. Use stdlib urllib with POST body.
• ❌ Don't send PNG to Qwen3-VL — JPEG q=90 is 5–10× smaller with no impact on VLM accuracy.
• ❌ Don't ask any tier to grade or solve. That's /grade's job; OCR must stay pure transcription.
• ❌ Don't trust Tier 1b (silent tesseract fallback) without reading the header — the file comment tells /grade to caveat its verdict.

Integration

• Called by /grade via answer-processing skill step 2
• Called by /ingest (future) for hand-written lecture notes, if any appear in materials/
• Writes to answers/converted/ only; does not modify originals in answers/