- 새 모듈 `crates/kebab-parse-image/src/ocr.rs` 추가. spec 의 `OcrEngine`
trait 그대로 + `OllamaVisionOcr` default 구현 + `apply_ocr` 헬퍼.
- `OllamaVisionOcr`: `<endpoint>/api/generate` 비스트리밍 호출,
`images: [base64]` 필드로 이미지 전달, 프롬프트는 언어 힌트
+ 화이트리스트 언어 목록 포함. 응답 prose 를 `OcrText.joined` 로,
prepared image 전체 영역 단일 region (confidence 1.0) 으로 wrap.
기본 모델 `gemma4:e4b`. endpoint 비어 있으면 `models.llm.endpoint`
로 fallback.
- 이미지 전처리: long-edge `config.image.ocr.max_pixels` (기본 1600,
256~4096 클램프) 초과 시 PNG 로 재인코딩 (image::imageops::resize,
Triangle filter). PNG 입력이 max 이내면 zero-copy passthrough.
- `apply_ocr` 는 OCR 성공 시 block.ocr 를 Some 으로 채우고
ProvenanceKind::OcrApplied 이벤트 추가. 실패 시 block.ocr 는
None 그대로 + provenance 미기록 (부분 상태 누출 금지).
- `kebab-config`: 새 `ImageCfg.ocr: OcrCfg` 블록 (enabled/engine/model
/endpoint/languages/max_pixels). `#[serde(default)]` 로 pre-P6
TOML 호환. `KEBAB_IMAGE_OCR_*` 환경변수 5종 추가.
## Spec deviation
원래 P6-2 spec 은 Tesseract 를 default OCR 엔진으로 지정했으나, dev /
CI 호스트에서 `libtesseract-dev` 시스템 패키지 설치를 피하려고
Ollama-vision 으로 default 를 교체. `OcrEngine` trait 추상화는 spec
그대로 보존 — Tesseract / Apple Vision / PaddleOCR 어댑터는 같은
trait 으로 추후 feature-gate 추가 가능. 자세한 내역은
`tasks/HOTFIXES.md` 2026-05-02 항목 참조.
Trust 측면: vision LM 은 hallucinate 가능. `OcrText.engine = "ollama-vision"`
필드로 consumer 가 엔진 별 신뢰 분기 가능.
## 테스트
- 신규 (`tests/ocr.rs`, 8 + 1 ignored):
- 200 happy → OcrText 디코딩 (joined / engine / engine_version /
region count / bbox / confidence)
- 빈 응답 → 빈 regions
- 5xx → Err with status + body 포함
- 200 error envelope → Err
- apply_ocr → block.ocr Some + Provenance OcrApplied 1건
- apply_ocr error → block.ocr None 유지 + events 미기록
- 4000×3000 PNG → max_pixels=1024 까지 다운스케일, aspect ratio 보존
- from_parts max_pixels 클램프
- opt-in `KEBAB_OCR_INTEGRATION=1` 통합 (실제 192.168.0.47 Ollama
`gemma4:e4b` 로 \"Hello World 2026\" 전사 검증 완료)
- 신규 (`src/ocr.rs` unit): truncate, build_prompt 언어/힌트 처리
- `kebab-config` 테스트 +3: defaults, env override, pre-P6 TOML 호환
전체: `cargo test -p kebab-parse-image` 28 pass + 1 ignored,
`cargo test -p kebab-config` 20 pass,
`cargo clippy --workspace --all-targets -- -D warnings` pass.
contract: docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
sections: §3.4 ImageRefBlock.ocr, §3.7a OcrText / OcrRegion, §9.1 OCR
vs caption provenance.
Define OcrEngine trait + a Tesseract-backed default implementation. Populate ImageRefBlock.ocr with OcrText { joined, regions, engine, engine_version }. Provide an apple-vision feature gate that switches to a sidecar binary on macOS.
Why now / why this size
Strict separation of OCR (observed text) from caption (model-generated). Confining engine choice to a single trait + adapter lets us swap to Apple Vision or PaddleOCR without touching the extractor or chunker.
Languages from config.ocr.languages (default ["eng", "kor"]).
Recognition produces OcrRegion { bbox: (x, y, w, h), text, confidence } for each "word" or "line" (configurable; default "line").
Drop regions with confidence < config.ocr.min_confidence (default 60.0). If all dropped, return OcrText { joined: "", regions: vec![], engine, engine_version }.
joined = regions.iter().map(|r| r.text).join(" ") (no smart layout reconstruction in v1).
engine = "tesseract", engine_version = <tesseract version string>. The tesseract crate (0.13+) does NOT expose a stable Rust version() accessor. Use one of: (a) call libtesseract's TessVersion() via the bundled FFI surface, OR (b) at adapter construction, shell-out tesseract --version once and cache the parsed "5.3.4"-style string. Both are deterministic for a fixed install. Pin the chosen approach in the implementation PR.
Apple Vision sidecar (feature apple-vision):
Spawn a small Swift binary kebab-vision-ocr (path from config.ocr.apple_vision_binary) feeding the image via stdin and reading JSON { regions: [{x,y,w,h,text,confidence}, ...] } from stdout.
Same threshold and joined rules as Tesseract. engine = "apple-vision", engine_version = sidecar's --version.
This subagent task does NOT write the Swift sidecar; it only wires the Rust side. Document the expected sidecar interface in docs/spec/sidecar-vision.md (separate doc spec stub, optional).
apply_ocr calls engine.recognize, sets block.ocr = Some(text), and appends a Provenance::OcrApplied event in the caller's CanonicalDocument (caller responsibility — this task exposes a helper).
Streaming / large images: cap decoded image size at 8192×8192 before passing to OCR; downscale with image::imageops::resize if larger.
Trust: OcrText is observed text (high trust). Captions (ModelCaption) are NOT generated here.
Determinism: Tesseract is deterministic for a fixed input + fixed page-segmentation mode; apply_ocr asserts this by calling twice in dev tests. Apple Vision is also deterministic in practice but may vary across macOS versions; document this and accept.
Storage / wire effects
None.
Test plan
kind
description
fixture / data
unit
Tesseract recognizes English on fixtures/image/hello-world.png (joined contains "hello world")
fixture
unit
confidence threshold drops noise regions
fixture with low-quality text
unit
Korean text recognized when kor language enabled
fixtures/image/안녕.png
unit
empty result returns OcrText { joined: "", regions: [], .. } not error
fixtures/image/no-text.png
unit
apply_ocr mutates block.ocr from None → Some
inline
determinism
two runs of recognize on same input → identical OcrText