diff --git a/klausur-service/backend/cv_vocab_pipeline.py b/klausur-service/backend/cv_vocab_pipeline.py
index 2a9ae7c..7c2d09d 100644
--- a/klausur-service/backend/cv_vocab_pipeline.py
+++ b/klausur-service/backend/cv_vocab_pipeline.py
@@ -903,8 +903,16 @@ def dewarp_image(img: np.ndarray, use_ensemble: bool = True) -> Tuple[np.ndarray
         detections[3]["confidence"] if len(detections) > 3 else 0.0,
     )
 
+    # Always include individual detections (even when no correction applied)
+    _all_detections = [
+        {"method": d["method"], "shear_degrees": d["shear_degrees"],
+         "confidence": d["confidence"]}
+        for d in detections
+    ]
+
     # Higher thresholds: subtle shear (<0.15°) is irrelevant for OCR
     if abs(shear_deg) < 0.15 or confidence < 0.5:
+        no_correction["detections"] = _all_detections
         return img, no_correction
 
     # Apply correction (negate the detected shear to straighten)
@@ -914,22 +922,14 @@ def dewarp_image(img: np.ndarray, use_ensemble: bool = True) -> Tuple[np.ndarray
     if not _dewarp_quality_check(img, corrected):
         logger.info("dewarp: quality gate REJECTED correction (%.3f°) — "
                      "projection variance did not improve", shear_deg)
-        no_correction["detections"] = [
-            {"method": d["method"], "shear_degrees": d["shear_degrees"],
-             "confidence": d["confidence"]}
-            for d in detections
-        ]
+        no_correction["detections"] = _all_detections
         return img, no_correction
 
     info = {
         "method": method,
         "shear_degrees": shear_deg,
         "confidence": confidence,
-        "detections": [
-            {"method": d["method"], "shear_degrees": d["shear_degrees"],
-             "confidence": d["confidence"]}
-            for d in detections
-        ],
+        "detections": _all_detections,
     }
 
     return corrected, info