Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM:
legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint,
a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual
example outputs, and quantization/concurrency benchmarks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/cer_divergence.py

@@ -0,0 +1,59 @@
+"""Character error rate (CER) between a reference baseline and a candidate.
+
+CER = levenshtein(reference, hypothesis) / len(reference).
+Used to gate work-reduction changes: keep a change only if CER <= 0.005.
+
+Usage:
+    python scripts/cer_divergence.py baseline_outputs/ candidate_outputs/
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def _levenshtein(a: str, b: str) -> int:
+    if a == b:
+        return 0
+    if not a:
+        return len(b)
+    if not b:
+        return len(a)
+    prev = list(range(len(b) + 1))
+    for i, ca in enumerate(a, 1):
+        cur = [i]
+        for j, cb in enumerate(b, 1):
+            cost = 0 if ca == cb else 1
+            cur.append(min(prev[j] + 1, cur[j - 1] + 1, prev[j - 1] + cost))
+        prev = cur
+    return prev[-1]
+
+
+def cer(reference: str, hypothesis: str) -> float:
+    if not reference:
+        return 0.0 if not hypothesis else 1.0
+    return _levenshtein(reference, hypothesis) / len(reference)
+
+
+def cer_over_dirs(baseline_dir: Path, candidate_dir: Path) -> dict:
+    per_file = {}
+    for ref_path in sorted(baseline_dir.glob("*.txt")):
+        cand_path = candidate_dir / ref_path.name
+        ref = ref_path.read_text(encoding="utf-8")
+        hyp = cand_path.read_text(encoding="utf-8") if cand_path.exists() else ""
+        per_file[ref_path.name] = cer(ref, hyp)
+    mean = sum(per_file.values()) / len(per_file) if per_file else 0.0
+    return {"mean_cer": mean, "max_cer": max(per_file.values(), default=0.0), "per_file": per_file}
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="CER divergence between two output dirs.")
+    parser.add_argument("baseline_dir", type=Path)
+    parser.add_argument("candidate_dir", type=Path)
+    args = parser.parse_args()
+    print(json.dumps(cer_over_dirs(args.baseline_dir, args.candidate_dir), indent=2))
+
+
+if __name__ == "__main__":
+    main()