Phase 1 MVP: synchronous OCR + regex header extraction

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>

tests/unit/test_preprocess.py

@@ -0,0 +1,37 @@
+"""Smoke tests for the preprocessing pipeline."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from ocr_sprint.pipeline.preprocess import PreprocessConfig, preprocess
+
+
+def test_preprocess_returns_bgr_uint8(blank_bgr_image: np.ndarray) -> None:
+    out = preprocess(blank_bgr_image)
+    assert out.dtype == np.uint8
+    assert out.ndim == 3
+    assert out.shape[2] == 3
+
+
+def test_preprocess_resizes_to_max_side() -> None:
+    big = np.full((4000, 3000, 3), 255, dtype=np.uint8)
+    cfg = PreprocessConfig(max_side=1000, denoise=False, deskew=False)
+    out = preprocess(big, cfg)
+    assert max(out.shape[:2]) == 1000
+
+
+def test_preprocess_does_not_upscale_small_images() -> None:
+    small = np.full((400, 300, 3), 255, dtype=np.uint8)
+    cfg = PreprocessConfig(max_side=2200, denoise=False, deskew=False)
+    out = preprocess(small, cfg)
+    assert out.shape[:2] == (400, 300)
+
+
+def test_adaptive_threshold_produces_binary_image() -> None:
+    img = np.random.randint(0, 256, (200, 200, 3), dtype=np.uint8)
+    cfg = PreprocessConfig(denoise=False, deskew=False, adaptive_threshold=True)
+    out = preprocess(img, cfg)
+    # adaptive threshold should leave only 0s and 255s
+    unique = np.unique(out)
+    assert set(unique.tolist()).issubset({0, 255})