diff --git a/nextcloud_mcp_server/document_processors/classifier.py b/nextcloud_mcp_server/document_processors/classifier.py
index 5af987d4..720f679c 100644
--- a/nextcloud_mcp_server/document_processors/classifier.py
+++ b/nextcloud_mcp_server/document_processors/classifier.py
@@ -43,8 +43,10 @@ logger = logging.getLogger(__name__)
 # so the pass stays bounded regardless of page count.
 MAX_SAMPLED_PAGES = 24
 
-# A page counts as "scanned-like" when a raster image covers most of it.
-IMAGE_COVERAGE_SCANNED = 0.80
+# Raster-image coverage above which a page raises the DIAGNOSTIC ``image_heavy``
+# flag. This is observability only -- it does NOT route to OCR (see module
+# docstring); routing is on the text signals alone.
+IMAGE_HEAVY_THRESHOLD = 0.80
 # Text-quality score below which the layer is treated as junk (mashed tokens).
 # Kept in sync with the DOCUMENT_OCR_MIN_TEXT_QUALITY setting default so the
 # module/diagnostic default matches production (the registry always passes the
@@ -185,7 +187,7 @@ def classify_pdf(content: bytes) -> DocClassification:
     # one full-page photo is flagged image_heavy yet still routes "fast" -- the
     # flag_total{image_heavy} count is expected to exceed classified{ocr}.
     flags: set[str] = set()
-    if any(p.image_coverage >= IMAGE_COVERAGE_SCANNED for p in pages):
+    if any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages):
         flags.add("image_heavy")
     if (
         ocr_frac >= OCR_PAGE_FRACTION
@@ -316,7 +318,7 @@ def classify_from_text(
             flags.add("scanned")
         elif mean_quality < min_text_quality:
             flags.add("bad_text_layer")
-    if any(p.image_coverage >= IMAGE_COVERAGE_SCANNED for p in pages):
+    if any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages):
         flags.add("image_heavy")
 
     recommended = "ocr" if ocr_frac >= page_fraction else "fast"
diff --git a/tests/unit/test_doc_classifier.py b/tests/unit/test_doc_classifier.py
index 1e57a91a..b80f7b52 100644
--- a/tests/unit/test_doc_classifier.py
+++ b/tests/unit/test_doc_classifier.py
@@ -118,6 +118,23 @@ def _image_with_mashed_text_pdf(pages: int = 2) -> bytes:
     return data
 
 
+def _image_with_clean_text_pdf(pages: int = 2) -> bytes:
+    # Full-page image with a CLEAN embedded text layer -- a scan carrying a good
+    # OCR layer, or a figure-heavy digital page. Image-heavy but usable text.
+    doc = pymupdf.open()
+    pix = pymupdf.Pixmap(pymupdf.csRGB, pymupdf.IRect(0, 0, 600, 850))
+    pix.clear_with(255)
+    img = pix.tobytes("png")
+    del pix  # Pixmap holds native memory; release it before the loop
+    for _ in range(pages):
+        page = doc.new_page(width=595, height=842)
+        page.insert_image(page.rect, stream=img)
+        page.insert_text((50, 60), "Hello world this is clean text. " * 8)
+    data: bytes = doc.tobytes()
+    doc.close()
+    return data
+
+
 def test_scanned_flag_when_no_text_layer():
     c = clf.classify_pdf(_full_page_image_pdf())
     assert c.total_chars == 0
@@ -250,6 +267,16 @@ def test_image_heavy_clean_text_stays_fast():
     assert all(p.needs_ocr is False for p in c.pages)
 
 
+def test_classify_pdf_image_heavy_clean_text_stays_fast():
+    # classify_pdf symmetry with test_image_heavy_clean_text_stays_fast: full-page
+    # raster images WITH a clean embedded text layer are image_heavy but route
+    # fast -- coverage is diagnostic, not routing, on the classify_pdf path too.
+    c = clf.classify_pdf(_image_with_clean_text_pdf())
+    assert c.recommended_tier == "fast"
+    assert "image_heavy" in c.flags
+    assert c.mean_text_quality >= clf.MIN_TEXT_QUALITY
+
+
 def test_scan_signal_ignored_when_coverage_low():
     full, bounds = _two_page(_CLEAN, _CLEAN)
     c = clf.classify_from_text(full, bounds, image_coverage=[0.1, 0.0])