ca0c0a0428
Implements the foundation of the OCR Sprint service: - FastAPI app with /api/v1/health and /api/v1/documents (sync upload) - Pydantic v2 schemas for documents, extraction result, personnel - Pipeline: PDF/image ingest (PyMuPDF), preprocessing (resize, deskew, denoise, optional adaptive threshold), PaddleOCR wrapper, regex-based header extraction (nomor sprint, tanggal, satuan, perihal, dasar), signatory NRP, master-pangkat validation, confidence scoring + routing. - Tests: 61 unit tests covering regex rules, validators, preprocess, ingest, confidence, and API contract (PaddleOCR mocked). - Tooling: pyproject (setuptools), ruff, mypy strict, pytest, pre-commit, Dockerfile, docker-compose, Makefile. - Docs: README + docs/architecture.md (full hybrid stack rationale and 6-phase roadmap). Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com>
124 lines
4.1 KiB
Markdown
124 lines
4.1 KiB
Markdown
# OCR Sprint Service
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OCR + structured extraction service for Indonesian police "surat sprint" (surat perintah) documents. Built around **FastAPI + PaddleOCR + hybrid extraction (regex → LLM lokal → validation)** with **on-premise** deployment as a hard requirement.
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> **Status:** Phase 1 MVP — synchronous PDF/image OCR with regex header extraction, validation, and confidence scoring. Phase 2–6 (document detection, table extraction, async pipeline, LLM extraction, HITL) are tracked in [`docs/architecture.md`](docs/architecture.md).
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## Why this stack
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- **PaddleOCR** is the strongest open-source OCR for mixed-language documents and runs fully on-prem (essential for police data).
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- **PP-Structure** (Phase 3) handles personnel tables natively.
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- **Regex-first, LLM-fallback extraction** keeps deterministic fields fast and predictable while letting an LLM handle format drift across Polri units.
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- **CPU-friendly defaults**: a small (1.5B–4B) local LLM via Ollama is the recommended default; the architecture is also GPU-ready.
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See [`docs/architecture.md`](docs/architecture.md) for the full architecture, accuracy expectations, and roadmap.
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## Quickstart
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### Prerequisites
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- Python **3.10–3.12**
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- ~3 GB free disk for PaddleOCR model downloads on first run
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- Linux/macOS recommended (Windows works but PaddleOCR install can be finicky)
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### Install (local dev)
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```bash
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git clone https://github.com/Adriankf59/ocr-sprint-service.git
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cd ocr-sprint-service
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python -m venv .venv && source .venv/bin/activate
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make install # installs runtime + dev deps + pre-commit
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cp .env.example .env # edit if you need GPU / different storage path
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```
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### Run the API
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```bash
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make dev
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# → http://localhost:8000/docs
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```
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### Try it out
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```bash
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curl -F "file=@samples/pdf/example.pdf" http://localhost:8000/api/v1/documents | jq
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```
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Expected response (truncated):
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```json
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{
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"job_id": "8f2a...",
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"status": "completed",
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"confidence": 0.93,
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"data": {
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"header": {
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"nomor_sprint": "Sprin/123/IV/2025/Reskrim",
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"tanggal": "2025-04-21",
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"satuan_penerbit": "KEPOLISIAN RESOR BANDUNG",
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"perihal": "Pelaksanaan penyelidikan kasus pencurian",
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"dasar": ["Undang-Undang Nomor 2 Tahun 2002 ...", "..."]
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},
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"personel": [],
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"ttd": { "nrp": "12345678" }
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},
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"review_flags": []
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}
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```
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> **Note:** Phase 1 does not yet populate the `personel[]` table — that requires PP-Structure (Phase 3). Header fields, signatory NRP, confidence, and HITL routing are fully wired.
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### Docker
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```bash
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docker compose build
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docker compose up -d
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docker compose logs -f api
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```
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The first request will trigger PaddleOCR to download its detection/recognition/cls models (~200 MB) into the `paddle-models` volume.
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## Development
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```bash
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make fmt # format with ruff
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make lint # lint
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make typecheck # mypy strict mode
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make test # pytest
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make test-cov # pytest + coverage
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```
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Pre-commit hooks run ruff on every commit. Install once with `pre-commit install` (already done by `make install`).
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## Project layout
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```
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src/ocr_sprint/
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api/ # FastAPI routes + error handlers
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schemas/ # Pydantic v2 models (request/response, extraction, personnel)
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pipeline/ # ingest → preprocess → ocr → extract → validate → score
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extract/ # regex_rules.py (Phase 1) → llm.py (Phase 5)
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data/ # master data (Polri ranks, etc.)
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utils/ # logging, helpers
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config.py # pydantic-settings
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main.py # app factory
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tests/unit/ # ~60 unit tests, no PaddleOCR dependency
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docs/ # architecture & decision records
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```
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## Roadmap
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| Phase | Scope | Status |
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| 1 | Sync API, PDF/image ingest, basic preprocessing, PaddleOCR, regex header extraction, validation, confidence scoring | **In progress** |
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| 2 | DocTR document detection + dewarping for phone photos | Planned |
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| 3 | PP-Structure table extraction for personnel rows | Planned |
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| 4 | Async pipeline (Celery + Redis), Postgres + MinIO, auth, observability | Planned |
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| 5 | LLM hybrid extraction (Ollama + structured output) | Planned |
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| 6 | HITL review endpoints + audit trail | Planned |
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## License
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Proprietary — internal use only.
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