Fix red false-positive in color detection for scanned black text

Scanner artifacts on black text produce slight warm tint (hue ~0, sat ~60)
that was misclassified as red. Now requires median_sat >= 80 specifically
for red classification, since genuine red text always has high saturation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

klausur-service/backend/cv_color_detect.py

@@ -178,6 +178,15 @@ def detect_word_colors(
             sat_pixels = text_pixels[text_pixels[:, 1] > sat_threshold]
             median_hue = float(np.median(sat_pixels[:, 0]))
             name = _hue_to_color_name(median_hue)
+
+            # Red requires higher saturation — scanner artifacts on black
+            # text often produce a slight warm tint (hue ~0) with low
+            # saturation that would otherwise be misclassified as red.
+            if name == "red" and median_sat < 80:
+                wb["color"] = _COLOR_HEX["black"]
+                wb["color_name"] = "black"
+                continue
+
             wb["color"] = _COLOR_HEX.get(name, _COLOR_HEX["black"])
             wb["color_name"] = name
             colored_count += 1

klausur-service/backend/tests/test_grid_editor_api.py

@@ -11,6 +11,8 @@ Covers:
 import sys
 sys.path.insert(0, '/app')
 
+import cv2
+import numpy as np
 import pytest
 from cv_vocab_types import PageZone, DetectedBox
 from grid_editor_api import (
@@ -912,3 +914,43 @@ class TestSlashIpaConversion:
         """tile /tail/ → tile [tˈaɪl]."""
         result = self._run_step_5h("tile /tail/ Nomen Dachziegel")
         assert "[tˈaɪl]" in result
+
+
+# ---------------------------------------------------------------------------
+# Color detection: red false-positive suppression
+# ---------------------------------------------------------------------------
+
+class TestRedFalsePositiveSuppression:
+    """Red requires median_sat >= 80 to avoid scanner artifact false positives."""
+
+    def test_low_saturation_red_classified_as_black(self):
+        """Black text with slight warm scanner tint (sat ~60) → black, not red."""
+        import numpy as np
+        from cv_color_detect import detect_word_colors
+
+        # Create a 40x20 image with dark gray pixels (slight warm tint)
+        # HSV: hue=5 (red range), sat=60 (above 55 threshold but below 80), val=40
+        img_hsv = np.full((40, 200, 3), [5, 60, 40], dtype=np.uint8)
+        img_bgr = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
+
+        wb = [{"left": 10, "top": 5, "width": 50, "height": 20, "text": "test"}]
+        detect_word_colors(img_bgr, wb)
+        assert wb[0]["color_name"] == "black", \
+            f"Expected black, got {wb[0]['color_name']} (scanner artifact false positive)"
+
+    def test_high_saturation_red_classified_as_red(self):
+        """Genuinely red text (sat=150) → red."""
+        import numpy as np
+        from cv_color_detect import detect_word_colors
+
+        # White background with red text region
+        # Background: white (H=0, S=0, V=255)
+        img_hsv = np.full((40, 200, 3), [0, 0, 255], dtype=np.uint8)
+        # Text area: red (H=5, S=180, V=200)
+        img_hsv[8:18, 15:55] = [5, 180, 200]
+        img_bgr = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
+
+        wb = [{"left": 10, "top": 5, "width": 50, "height": 20, "text": "red"}]
+        detect_word_colors(img_bgr, wb)
+        assert wb[0]["color_name"] == "red", \
+            f"Expected red, got {wb[0]['color_name']}"