Add containerized OCR pipeline for fuel receipts

Self-hosted, verifiable pipeline for extracting structured data (station,
date/time, price per litre, litres, discount, total) from German fuel
receipt photos: grayscale + mild bilateral filter + contrast stretch,
Tesseract OCR, then LLM structuring via a small local model (Qwen 2.5,
served by Ollama). Each step is independently runnable and inspectable
through numbered scripts against a shared tooling container.

Ollama runs as its own long-lived container (0_run_ollama.sh), separate
from the stateless per-receipt pipeline steps, so the model stays loaded
in memory between runs. Includes a containerized ground-truth batch test
(test_batch.sh) that reproduces the full pipeline over the test receipts
and reports per-field accuracy.

Targets eventual deployment on a Raspberry Pi 4 k3s cluster.

tooling-container/src/ocr.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+"""Step 2: run Tesseract OCR (German) over a preprocessed receipt image,
+writing the raw recognized text to the output directory for inspection
+before it is handed to the structuring step."""
+import argparse
+import os
+
+import pytesseract
+from PIL import Image
+
+
+def ocr(path: str, out_dir: str) -> str:
+    os.makedirs(out_dir, exist_ok=True)
+    img = Image.open(path)
+    text = pytesseract.image_to_string(img, lang="deu", config="--psm 3")
+
+    out_path = os.path.join(out_dir, "ocr.txt")
+    with open(out_path, "w", encoding="utf-8") as f:
+        f.write(text)
+    return out_path
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-in", dest="in_path", required=True)
+    parser.add_argument("-out", dest="out_dir", required=True)
+    args = parser.parse_args()
+
+    out_path = ocr(args.in_path, args.out_dir)
+    print(out_path)
+
+
+if __name__ == "__main__":
+    main()