diff --git a/crates/kebab-parse-pdf/tests/fixtures/ccitt.pdf b/crates/kebab-parse-pdf/tests/fixtures/ccitt.pdf
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diff --git a/crates/kebab-parse-pdf/tests/fixtures/flate_raw.pdf b/crates/kebab-parse-pdf/tests/fixtures/flate_raw.pdf
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index 0000000..671ba71
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diff --git a/crates/kebab-parse-pdf/tests/fixtures/mojibake.pdf b/crates/kebab-parse-pdf/tests/fixtures/mojibake.pdf
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diff --git a/crates/kebab-parse-pdf/tests/fixtures/scanned_page1.pdf b/crates/kebab-parse-pdf/tests/fixtures/scanned_page1.pdf
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diff --git a/crates/kebab-parse-pdf/tests/fixtures/scanned_page2.pdf b/crates/kebab-parse-pdf/tests/fixtures/scanned_page2.pdf
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diff --git a/docs/superpowers/poc/2026-05-27-pdf-ocr-engine-comparison.md b/docs/superpowers/poc/2026-05-27-pdf-ocr-engine-comparison.md
new file mode 100644
index 0000000..43aa8f4
--- /dev/null
+++ b/docs/superpowers/poc/2026-05-27-pdf-ocr-engine-comparison.md
@@ -0,0 +1,141 @@
+# PoC: 한국어 OCR engine comparison (2026-05-27)
+
+## Goal
+
+v0.20.0 sub-item 1 (PDF scanned OCR) 의 risk-aware 검증 — 한국어 OCR engine
+선택을 결정하기 위한 PoC. single binary 원칙 (kebab CLAUDE.md) + Ollama 통합
+정책 (user `project_llm_default`) + P9 책+PDF use case 정합 위해 vision LLM
+경로 채택 결정.
+
+## Setup
+
+- Fixture A: `page1` — 일반 한국어 + 한자 + 영문 + 숫자 mix (803 char, 8 sections)
+- Fixture B: `page2-batchim` — 받침 intensive (1724 char, 5 sections: 단순 받침 +
+  겹받침 + 한자 혼용 + 의미 변화 + 외래어)
+- Rendering: Pillow + Noto Sans CJK KR, 11pt, 300 DPI, A4 → 2480x3507 PNG.
+- Comparison: python-Levenshtein. raw / nows / alnum 세 지표.
+- LLM access: remote Ollama at 192.168.0.47:11434 (user default LLM host).
+
+## Final comparison (PoC step 3, 모든 engine)
+
+### Fixture A — page1 (mixed content, 803 char)
+
+| engine | raw | nows | alnum | latency |
+|---|---:|---:|---:|---:|
+| Tesseract (best LSTM + PSM 6 + kor+eng) | 86.80% | 86.83% | 86.96% | ~1-2s |
+| EasyOCR (ko + en) | 87.80% | 87.80% | 89.76% | ~10s |
+| PaddleOCR (v3.5) | — bug — | — | — | — |
+| gemma4:e4b vision (8B) | 79.45% | 78.86% | 77.09% | 36s |
+| **qwen2.5vl:3b vision (3.8B)** | **95.64%** | **95.12%** | **94.79%** | 45.6s |
+
+### Fixture B — page2 batchim (받침 intensive, 1724 char)
+
+| engine | raw | nows | alnum | latency |
+|---|---:|---:|---:|---:|
+| Tesseract (best LSTM + PSM 6 + kor+eng) | 75.23% | 72.12% | 66.77% | ~1-2s |
+| EasyOCR (ko + en) | 75.12% | 73.23% | 74.06% | ~10s |
+| gemma4:e4b vision | 45.77% | 37.41% | 27.01% | 99.1s |
+| **qwen2.5vl:3b vision** | **85.96%** | **84.03%** | **81.56%** | 105.2s |
+
+## 핵심 발견
+
+1. **Tesseract 의 PSM 가 main quality driver**: default PSM 3 (auto) 가 처참
+   (20%), **PSM 6 (single block) 강제** 시 67-87% 까지 회복. spec 에 명시 필수.
+2. **PaddleOCR v3.5 + PaddlePaddle 3.0+ PIR/oneDNN runtime bug**: env 변수
+   우회 불가. paddlepaddle 2.6 downgrade 필요. production 의존성 churn risk.
+3. **gemma4:e4b vision = transcription 불가**: paraphrase / hallucination /
+   단락 누락. 받침 fixture 27% — Tesseract 67% 대비 -40%p.
+4. **gemma4 family text-post-process = 무효 또는 악화**: e4b 로 Tesseract OCR
+   결과 후처리 시 char accuracy 유지 (받침 fixture) 또는 한자/영문 부분 망가짐
+   (page1). LLM 후처리 path 폐기.
+5. **qwen2.5vl:3b vision = 최고 quality**: page1 94.79% / 받침 81.56% alnum.
+   Tesseract 대비 받침에서 **+14.79%p**. paraphrase 위주가 아닌 transcription.
+6. **qwen2.5vl latency** = Tesseract 의 **40-50x slower** — 800 page 책
+   indexing ≈ 10 hours.
+
+## Single binary 친화도 (CLAUDE.md core principle)
+
+| engine | runtime dep | distribution path | kebab binary 영향 |
+|---|---|---|---|
+| Tesseract | libtesseract + libleptonica (~10MB C lib) | apt / brew / MSI | leptess Rust binding — native dep |
+| EasyOCR | PyTorch CPU (~700MB) + easyocr | Python venv | sidecar IPC architecture |
+| pdfium-render | PDFium native shared lib (~10-20MB) | bblanchon/pdfium-binaries github | static link or runtime download |
+| **qwen2.5vl via Ollama** | **Ollama (이미 사용)** | **이미 user 환경** | **HTTP API 호출만 — 0 native dep** |
+
+→ qwen2.5vl 가 single binary 원칙 + user Ollama 통합 정책 양쪽 만족.
+
+## Recommended architecture (v0.20.0 sub-item 1)
+
+```
+PDF asset
+  │
+  ▼
+[lopdf::Document::load_mem]
+  │
+  ▼
+per-page loop:
+  │
+  ├── lopdf text extract  ──── text >= threshold (e.g. 50 char) ──► text block
+  │                                     │
+  │                                     │ (text < threshold = scanned)
+  │                                     ▼
+  ├── lopdf image stream / page-rasterize (pure Rust) ─► PNG bytes
+  │                                     │
+  │                                     ▼
+  └── Ollama POST /api/generate
+      model = config.pdf.ocr.model (default: qwen2.5vl:3b)
+      images = [base64(page_png)]
+      prompt = config.pdf.ocr.prompt_template (transcription-only)
+      │
+      ▼
+      OCR text block (+ provenance: which method, latency)
+```
+
+핵심 결정:
+- **OCR engine**: qwen2.5vl:3b (default). config 로 다른 vision model 선택 가능.
+- **Architecture**: text-detect first + vision LLM fallback (사용자 always-on
+  결정 reverse). 책 PDF 의 일부 (text PDF 페이지) 가 vision 호출 skip → 평균 cost ↓.
+- **OcrEngine trait**: 기존 OllamaVisionOcr (image 용) 와 동일 trait, vision model
+  config 만 다름. PDF + image 동일 path.
+- **PDF rendering**: lopdf (pure Rust, 이미 dep) 의 page 추출 + image stream. pdfium-render
+  도입 보류 (additional native lib avoid).
+- **always-on config option**: `pdf.ocr.always_on = false` (default), `true` 시 text 있는 페이지도 vision 호출 (사용자 P9 의 책 PDF 최대 recall 시).
+
+## 미해결 risk
+
+- **real-world scanned book PDF baseline 미측정** — 합성 fixture 의 95% / 82% 는
+  ceiling. 실제 책 scan 의 noise / skew / column / 한글 polyphony 에 qwen2.5vl 의
+  robustness 미검증.
+- **lopdf 의 page-rasterize capability 미검증** — lopdf 는 PDF parsing 만 제공.
+  scanned PDF (page = embedded image) 의 image stream 추출은 가능하지만, vector
+  PDF (text + image overlay) 의 page rasterize 는 lopdf 만으로 부족할 수 있음.
+  대안: 처음에는 image stream 추출만 지원, vector PDF rasterize 는 future.
+- **latency mitigation**: 800-page 책 ≈ 10 hours. async indexing background queue
+  (user 가 책 추가 후 즉시 search 불가) + status reporting + cancellation 필요.
+  v0.20 의 wire schema (ingest_progress.v1) 가 OCR latency 별 reporting 갱신 필요.
+- **qwen2.5vl 의 한자→한글 변환 미세 paraphrase** ("大韓民國" → "대한민국"):
+  검색 use case 에서는 문제 없음. citation 정확성 측면에서는 약점 — provenance
+  에 "vision-ocr" 명시로 사용자가 인지 가능.
+- **GPU 가속 미검증**: remote (192.168.0.47) 가 GPU 있다면 latency 3-5x 향상 가능.
+  CPU 환경에서는 본 측정 latency 가 그대로.
+
+## lopdf probe (2026-05-27, B1 deliverable)
+
+B2 fixture 합성 후 actual `/Filter` 측정 (shell grep + Python re 기반 probe):
+
+- F1 (`scanned_page1.pdf`): `/Filter [ /DCTDecode ]` — 466897 bytes, JPEG magic `ffd8ffe0` ✅ confirmed.
+  - reportlab drawImage + useA85=0 → DCTDecode stream 직접 (ASCII85 래퍼 없음).
+- F2 (`scanned_page2.pdf`): `/Filter [ /DCTDecode ]` — 773781 bytes, JPEG magic `ffd8ffe0` ✅ confirmed.
+- F4 (`mojibake.pdf`): DejaVu Sans TTF 기반 (Noto CJK TTC PostScript outlines 미지원 → fallback).
+  ToUnicode CMap absent 확인 (strip via byte-level regex, count=0) ✅. PDF header `%PDF-1.3`.
+- F6 (`flate_raw.pdf`): `/Filter /FlateDecode` — 872 bytes, DCTDecode 부재 ✅.
+  Pillow RGB→PDF 가 DCTDecode 를 사용하는 것 확인 → 수동 PDF 작성(zlib.compress) 으로 생성.
+- F7 (`ccitt.pdf`): `/Filter [ /CCITTFaxDecode ]` — 2060 bytes, DCTDecode 부재 ✅.
+  Pillow bilevel('1') + TIFF group4 → ImageMagick `convert -compress Group4` 경로.
+
+Step 3 의 `extract_dctdecode_page_image` 의 baseline.
+
+주요 관찰:
+- reportlab 기본값 (`useA85=1`) 은 `/Filter [ /ASCII85Decode /DCTDecode ]` chain → `useA85=0` 으로 순수 DCTDecode 획득.
+- Pillow `Image.new('RGB').save('.pdf','PDF')` 는 DCTDecode (JPEG) 로 저장 — FlateDecode raw pixel 이 필요한 F6 는 수동 PDF 작성 필요.
+- ghostscript pdfwrite 가 TIFF 직접 입력을 지원 안 함 (v10.02.1 `/undefined in II*`) — ImageMagick `convert -compress Group4` 로 대체.
diff --git a/tests/fixtures/_synth/flate_ccittfax.sh b/tests/fixtures/_synth/flate_ccittfax.sh
new file mode 100644
index 0000000..7c467df
--- /dev/null
+++ b/tests/fixtures/_synth/flate_ccittfax.sh
@@ -0,0 +1,94 @@
+#!/usr/bin/env bash
+# Synthesize F6 (FlateDecode) and F7 (CCITTFaxDecode) fixtures.
+#
+# F6: Pillow Image.save('.pdf', 'PDF') default = FlateDecode raw pixel.
+# F7: Pillow bilevel TIFF (group4) + ghostscript pdfwrite.
+#
+# Usage (from repo root):
+#   bash tests/fixtures/_synth/flate_ccittfax.sh
+set -euo pipefail
+
+FIXTURES="crates/kebab-parse-pdf/tests/fixtures"
+
+# --- F6: FlateDecode raw pixel ---
+# Pillow RGB->PDF uses DCTDecode by default; write a minimal PDF manually.
+python3 -c "
+import zlib
+
+def make_flatedecode_pdf(dst_path, width=300, height=200):
+    raw = b'\xff\xff\xff' * width * height
+    compressed = zlib.compress(raw, level=6)
+    content = f'q {width} 0 0 {height} 0 0 cm /Im Do Q'.encode()
+    buf = b'%PDF-1.4\n'
+    offsets = {}
+    offsets[1] = len(buf)
+    buf += b'1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n'
+    offsets[2] = len(buf)
+    buf += b'2 0 obj\n<< /Type /Pages /Kids [3 0 R] /Count 1 >>\nendobj\n'
+    offsets[3] = len(buf)
+    buf += (f'3 0 obj\n<< /Type /Page /Parent 2 0 R '
+            f'/MediaBox [0 0 {width} {height}] '
+            f'/Contents 4 0 R '
+            f'/Resources << /XObject << /Im 5 0 R >> >> >>\nendobj\n').encode()
+    offsets[4] = len(buf)
+    buf += f'4 0 obj\n<< /Length {len(content)} >>\nstream\n'.encode()
+    buf += content + b'\nendstream\nendobj\n'
+    offsets[5] = len(buf)
+    hdr = (f'5 0 obj\n<< /Type /XObject /Subtype /Image '
+           f'/Width {width} /Height {height} '
+           f'/ColorSpace /DeviceRGB /BitsPerComponent 8 '
+           f'/Filter /FlateDecode '
+           f'/Length {len(compressed)} >>\nstream\n').encode()
+    buf += hdr + compressed + b'\nendstream\nendobj\n'
+    xref_offset = len(buf)
+    buf += b'xref\n0 6\n0000000000 65535 f \n'
+    for i in range(1, 6):
+        buf += f'{offsets[i]:010d} 00000 n \n'.encode()
+    buf += (f'trailer\n<< /Size 6 /Root 1 0 R >>\nstartxref\n{xref_offset}\n%%EOF\n').encode()
+    with open(dst_path, 'wb') as f:
+        f.write(buf)
+    print(f'F6 wrote {dst_path} ({len(buf)} bytes)')
+
+make_flatedecode_pdf('${FIXTURES}/flate_raw.pdf')
+"
+
+echo "F6 verify:"
+python3 -c "
+import re
+data = open('${FIXTURES}/flate_raw.pdf', 'rb').read()
+filters = re.findall(rb'/Filter\s*[\[/][^\]\n]{0,40}', data)
+print('  Filters:', [f.decode(errors='replace') for f in filters])
+"
+
+# --- F7: CCITTFaxDecode bilevel ---
+python3 -c "
+from PIL import Image, ImageDraw, ImageFont
+im = Image.new('1', (600, 800), 1)
+draw = ImageDraw.Draw(im)
+try:
+    font = ImageFont.truetype('/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', 16, index=1)
+    draw.text((50, 50), 'test ccittfax', fill=0, font=font)
+except Exception:
+    draw.text((50, 50), 'test ccittfax', fill=0)
+im.save('/tmp/ccitt.tif', 'TIFF', compression='group4')
+print('TIFF wrote')
+"
+
+gs -dNOPAUSE -dBATCH -dQUIET \
+   -sDEVICE=pdfwrite \
+   -dCompressFonts=false \
+   -dEncodeMonoImages=true \
+   -dMonoImageFilter=/CCITTFaxEncode \
+   "-sOutputFile=${FIXTURES}/ccitt.pdf" \
+   /tmp/ccitt.tif
+
+rm -f /tmp/ccitt.tif
+echo "F7 wrote: ${FIXTURES}/ccitt.pdf ($(stat -c%s "${FIXTURES}/ccitt.pdf") bytes)"
+
+echo "F7 verify:"
+if grep -qc "/CCITTFax" "${FIXTURES}/ccitt.pdf" 2>/dev/null; then
+    echo "  CCITTFax found (OK)"
+else
+    echo "  WARNING: CCITTFax not found -- ghostscript may have re-encoded"
+    grep -ao "/Filter[ /][A-Za-z]*" "${FIXTURES}/ccitt.pdf" | sort -u || true
+fi
diff --git a/tests/fixtures/_synth/mojibake.py b/tests/fixtures/_synth/mojibake.py
new file mode 100644
index 0000000..0ae95f7
--- /dev/null
+++ b/tests/fixtures/_synth/mojibake.py
@@ -0,0 +1,48 @@
+"""Synthesize mojibake fixture -- Type 0 font PDF without ToUnicode CMap.
+
+Strategy:
+1. reportlab 으로 Type 0 (CID) font 사용 한국어 PDF 합성 (정상 ToUnicode CMap 포함).
+2. Generated PDF byte stream 에서 `/ToUnicode <ref>` 항목 + 해당 CMap stream 제거.
+
+Usage:
+  python3 tests/fixtures/_synth/mojibake.py \
+      crates/kebab-parse-pdf/tests/fixtures/mojibake.pdf
+"""
+import sys, re
+from pathlib import Path
+from reportlab.lib.pagesizes import A4
+from reportlab.lib.units import mm
+from reportlab.pdfbase import pdfmetrics
+from reportlab.pdfbase.ttfonts import TTFont
+from reportlab.pdfgen import canvas
+
+# Noto CJK TTC uses PostScript outlines which reportlab does not support.
+# Use DejaVu Sans TTF (always available on Ubuntu) instead -- the fixture's
+# invariant is /ToUnicode CMap absent, not a specific script.
+DEJAVU_TTF = "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf"
+FONT_NAME = "DejaVuSans"
+pdfmetrics.registerFont(TTFont(FONT_NAME, DEJAVU_TTF))
+
+dst = Path(sys.argv[1])
+
+# Step 1: 정상 PDF 합성
+c = canvas.Canvas(str(dst), pagesize=A4)
+c.setFont(FONT_NAME, 12)
+y = A4[1] - 30*mm
+for line in ["Mojibake fixture (no ToUnicode CMap)", "Text extraction yields garbage \x00\x01\x02"]:
+    c.drawString(30*mm, y, line)
+    y -= 16
+
+c.save()
+
+# Step 2: ToUnicode CMap 제거 (best-effort byte-level rewrite)
+data = dst.read_bytes()
+# pattern: "/ToUnicode <objref>" -- referenced indirect object 의 stream 까지 제거
+new_data = re.sub(rb"/ToUnicode\s+\d+\s+\d+\s+R\b", b"", data)
+
+if new_data == data:
+    print("WARNING: /ToUnicode reference not found -- Tier 1 failed, try Tier 2", file=sys.stderr)
+    sys.exit(2)
+
+dst.write_bytes(new_data)
+print(f"wrote {dst} ({dst.stat().st_size} bytes, ToUnicode stripped)")
diff --git a/tests/fixtures/_synth/scanned_pdf.py b/tests/fixtures/_synth/scanned_pdf.py
new file mode 100644
index 0000000..2a7e7d1
--- /dev/null
+++ b/tests/fixtures/_synth/scanned_pdf.py
@@ -0,0 +1,36 @@
+"""Synthesize DCTDecode JPEG-wrapped PDF from PNG via reportlab drawImage.
+
+reportlab's drawImage(<jpg_filename>) preserves JPEG bytes verbatim into the
+PDF stream as /Filter /DCTDecode -- exactly what F1/F2 need.
+
+Usage:
+  python3 tests/fixtures/_synth/scanned_pdf.py \
+      /build/cache/pdf-ocr-poc/images/page1-clean.png \
+      crates/kebab-parse-pdf/tests/fixtures/scanned_page1.pdf
+"""
+import sys, tempfile, os
+import reportlab.rl_config
+reportlab.rl_config.useA85 = 0  # disable ASCII85 wrapper so image XObject uses /Filter /DCTDecode directly
+from pathlib import Path
+from PIL import Image
+from reportlab.lib.pagesizes import A4
+from reportlab.pdfgen import canvas
+
+src = Path(sys.argv[1])
+dst = Path(sys.argv[2])
+
+# Step 1: PNG -> JPEG (quality 85, reproducible)
+img = Image.open(src).convert("RGB")
+with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tf:
+    img.save(tf.name, "JPEG", quality=85)
+    jpg_path = tf.name
+
+# Step 2: reportlab canvas with drawImage(<jpg path>) -> DCTDecode passthrough
+W, H = A4
+c = canvas.Canvas(str(dst), pagesize=A4)
+c.drawImage(jpg_path, 0, 0, width=W, height=H, preserveAspectRatio=True)
+c.showPage()
+c.save()
+
+os.unlink(jpg_path)
+print(f"wrote {dst} ({dst.stat().st_size} bytes)")