diff --git a/klausur-service/backend/ocr_pipeline_api.py b/klausur-service/backend/ocr_pipeline_api.py
index 89f874b..11a88f2 100644
--- a/klausur-service/backend/ocr_pipeline_api.py
+++ b/klausur-service/backend/ocr_pipeline_api.py
@@ -3219,8 +3219,10 @@ def _merge_row_sequences(paddle_row: list, tess_row: list) -> list:
         # Same text or one contains the other
         is_same = (pt == tt) or (len(pt) > 1 and len(tt) > 1 and (pt in tt or tt in pt))
 
-        # Spatial overlap check: if words overlap >= 50% horizontally,
-        # they're the same physical word regardless of OCR text differences
+        # Spatial overlap check: if words overlap >= 40% horizontally,
+        # they're the same physical word regardless of OCR text differences.
+        # (40% catches borderline cases like "Stick"/"Stück" at 48% overlap)
+        spatial_match = False
         if not is_same:
             overlap_left = max(pw["left"], tw["left"])
             overlap_right = min(
@@ -3229,8 +3231,9 @@ def _merge_row_sequences(paddle_row: list, tess_row: list) -> list:
             )
             overlap_w = max(0, overlap_right - overlap_left)
             min_w = min(pw.get("width", 1), tw.get("width", 1))
-            if min_w > 0 and overlap_w / min_w >= 0.5:
+            if min_w > 0 and overlap_w / min_w >= 0.4:
                 is_same = True
+                spatial_match = True
 
         if is_same:
             # Matched — average coordinates weighted by confidence
@@ -3239,8 +3242,14 @@ def _merge_row_sequences(paddle_row: list, tess_row: list) -> list:
             total = pc + tc
             if total == 0:
                 total = 1
+            # Text: prefer higher-confidence engine when texts differ
+            # (e.g. Tesseract "Stück" conf=98 vs PaddleOCR "Stick" conf=80)
+            if spatial_match and pc < tc:
+                best_text = tw["text"]
+            else:
+                best_text = pw["text"]
             merged.append({
-                "text": pw["text"],  # Paddle text preferred
+                "text": best_text,
                 "left": round((pw["left"] * pc + tw["left"] * tc) / total),
                 "top": round((pw["top"] * pc + tw["top"] * tc) / total),
                 "width": round((pw["width"] * pc + tw["width"] * tc) / total),