fix(classifier): make image coverage diagnostic-only, not an OCR routing trigger

The tier-0 classifier escalated any page with raster-image coverage >=0.80 to
the OCR tier regardless of its text layer. On OHR-Bench this drove ~45% of all
OCR escalations: clean born-digital pages dominated by a figure, and scanned
pages that already carry a usable OCR text layer -- re-OCR adds nothing for
either, but each one was routed to the paid tier-3 OCR.

Route on the text signals only (near-empty or junk-quality layer). Image
coverage is still computed and still raises the `image_heavy` diagnostic flag,
but no longer routes. True scans with no/garbage text continue to escalate via
the empty-text and quality signals, so genuine OCR needs are unaffected.

Trade-off: image-only content on an otherwise-clean page (handwriting, stamps,
text inside figures) is no longer force-routed to OCR. This was previously
intentional; the escalation cost outweighed the benefit for RAG indexing.

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

nextcloud_mcp_server/document_processors/classifier.py

@@ -4,17 +4,24 @@ Decides which extraction tier a PDF should escalate to, from cheap signals:
   * text_quality -- is the text layer usable, or mashed/space-less junk? (the
     "Student 147" lesson: a text layer can exist yet be unusable, e.g.
     "01322234567mobile")
-  * image_coverage -- a page that is mostly a raster image is a scan/photo whose
-    content isn't fully in any text layer.
   * no text layer -- the strongest OCR signal available from text alone.
 
+Routing is on the TEXT signals only: a page escalates to OCR when its text is
+near-empty or junk-quality. ``image_coverage`` is computed but is a DIAGNOSTIC
+signal (the ``image_heavy`` flag), NOT a routing trigger: a mostly-raster page
+whose embedded text is already usable (a scan carrying a clean OCR layer, or a
+digital page dominated by a figure) gains nothing from re-OCR, so escalating it
+to the paid OCR tier was wasteful -- on OHR-Bench the coverage trigger drove
+~45% of escalations. The trade-off: image-only content on an otherwise-clean
+page (handwriting, stamps, figure text) is no longer force-routed to OCR.
+
 Two entry points:
   * ``classify_from_text(text, page_boundaries, ...)`` -- the HOT PATH. Routes on
     text-quality + near-empty pages derived from the tier-1 extraction (~no
     cost). When OCR + scan-detection are enabled the registry also passes
-    per-page ``image_coverage`` (from ``image_coverage_per_page``) so scans are
+    per-page ``image_coverage`` (from ``image_coverage_per_page``) for the
-    caught too; that image pass is the only added cost and only OCR-opted-in
+    ``image_heavy`` diagnostic flag; that image pass is the only added cost and
-    tenants pay it. Thresholds come from per-tenant settings.
+    only OCR-opted-in tenants pay it. Thresholds come from per-tenant settings.
   * ``classify_pdf(content)`` -- a standalone/diagnostic pass that re-opens the
     PDF and does image-coverage analysis inline. Off the hot path.
 
@@ -152,15 +159,15 @@ def classify_pdf(content: bytes) -> DocClassification:
             text = page.get_text("text")
             quality = _text_quality(text)
             coverage = _page_image_coverage(page)
-            # OCR-worthy on the same three signals as classify_from_text (kept in
+            # OCR-worthy on TEXT signals only (kept in sync with
-            # sync so an operator reproducing routing offline gets the pipeline's
+            # classify_from_text): a junk/low-quality text layer (the
-            # answer): a mostly-raster scan, a junk/low-quality text layer (the
+            # word-merging case) or an effectively empty one. Image coverage is
-            # word-merging case), or an effectively empty text layer.
+            # deliberately NOT a routing trigger -- a mostly-raster page whose
-            needs_ocr = (
+            # embedded text is already usable (a scan with a clean OCR layer, or
-                coverage >= IMAGE_COVERAGE_SCANNED
+            # a digital page dominated by a figure) gains nothing from re-OCR, so
-                or quality < MIN_TEXT_QUALITY
+            # routing it to the paid OCR tier was wasteful. High coverage still
-                or len(text.strip()) < MIN_PAGE_CHARS
+            # raises the diagnostic image_heavy flag below.
-            )
+            needs_ocr = quality < MIN_TEXT_QUALITY or len(text.strip()) < MIN_PAGE_CHARS
             pages.append(
                 PageSignals(n, len(text), round(coverage, 3), quality, needs_ocr)
             )
@@ -206,16 +213,13 @@ def classify_pdf(content: bytes) -> DocClassification:
 def image_coverage_per_page(content: bytes) -> list[float]:
     """Raster-image coverage in ``[0, 1]`` for every page (document order).
 
-    Lets the hot path flag scanned pages whose embedded text layer is junk but
+    Feeds the ``image_heavy`` DIAGNOSTIC flag only (coverage no longer routes --
-    statistically clean-looking. Re-opens the PDF, so the registry calls it only
+    see module docstring). Re-opens the PDF, so the registry calls it only when
-    when OCR + scan detection are enabled (the cost is borne by OCR-opted-in
+    OCR + scan detection are enabled (the cost is borne by OCR-opted-in tenants).
-    tenants). Returned list is aligned by index with the leading page boundaries.
+    Returned list is aligned by index with the leading page boundaries.
 
     Bounded to the first ``MAX_SAMPLED_PAGES`` pages -- the image pass is the
-    costly part, so a 200-page scan isn't fully rasterised on the hot path. Pages
+    costly part, so a 200-page scan isn't fully rasterised on the hot path.
-    beyond the cap fall back to the text-quality signal in ``classify_from_text``
-    (a scanned tail has junk text too), and ``page_fraction`` still gates over
-    every page.
     """
     import pymupdf  # noqa: PLC0415 -- keep the heavy import lazy
 
@@ -238,18 +242,18 @@ def classify_from_text(
     """Classify from text already extracted by tier-1 -- no PDF re-open by default.
 
     The hot-path classifier. A page is OCR-worthy when its text is near-empty
-    (``< min_page_chars``), its text-quality is junk (``< min_text_quality`` --
+    (``< min_page_chars``) or its text-quality is junk (``< min_text_quality`` --
-    the word-merging signal), OR (when ``image_coverage`` is supplied, i.e. OCR +
+    the word-merging signal). The doc recommends ``ocr`` once
-    scan-detection are on) the page is mostly a raster image. The doc recommends
+    ``ocr_frac >= page_fraction``. Thresholds are passed in by the registry from
-    ``ocr`` once ``ocr_frac >= page_fraction``. Thresholds are passed in by the
+    per-tenant settings. ``image_coverage`` (when supplied) only feeds the
-    registry from per-tenant settings.
+    ``image_heavy`` diagnostic flag -- it does NOT route (see module docstring).
 
     ``page_boundaries`` are ``{page, start_offset, end_offset}`` indexing into
     ``full_text``; ``image_coverage[i]`` (if given) aligns with the i-th boundary.
 
-    Note: the ``image_heavy`` flag (and the image-coverage trigger) are only set
+    Note: the ``image_heavy`` flag is only set when ``image_coverage`` is
-    when ``image_coverage`` is supplied, so for tenants with scan detection off
+    supplied, so for tenants with scan detection off that flag is always zero.
-    that flag is always zero -- the text-quality/empty signals still route.
+    Routing is unaffected either way -- it is on the text signals alone.
     """
     # image_coverage is expected to be one entry per page, capped at
     # MAX_SAMPLED_PAGES (see image_coverage_per_page). Any other length means the
@@ -278,11 +282,14 @@ def classify_from_text(
             if image_coverage is not None and idx < len(image_coverage)
             else 0.0
         )
-        needs_ocr = (
+        # Routing is on TEXT signals only: a near-empty layer or a junk/
-            len(seg.strip()) < min_page_chars
+        # space-mangled one. Image coverage (``cov``) is intentionally not a
-            or quality < min_text_quality
+        # routing trigger -- a mostly-raster page with an already-usable text
-            or cov >= IMAGE_COVERAGE_SCANNED
+        # layer does not benefit from re-OCR, so escalating it to the paid OCR
-        )
+        # tier was wasteful (on OHR-Bench this drove ~45% of escalations: clean
+        # digital figure-pages and scans that already carry a good OCR layer).
+        # ``cov`` still feeds the diagnostic ``image_heavy`` flag below.
+        needs_ocr = len(seg.strip()) < min_page_chars or quality < min_text_quality
         pages.append(
             PageSignals(b["page"], len(seg), round(cov, 3), quality, needs_ocr)
         )

tests/unit/test_doc_classifier.py

@@ -3,8 +3,10 @@
 Pins the routing decisions and the text-quality heuristic that drive which
 extraction tier a PDF starts in:
   * a clean born-digital PDF (text, no full-page images) -> ``fast`` (tier 1);
-  * a full-page-image scan -> ``ocr`` (tier 3), since handwriting/stamps aren't
+  * a full-page-image scan with no usable text layer -> ``ocr`` (tier 3);
-    in any text layer;
+  * routing is on TEXT signals only -- image coverage feeds the ``image_heavy``
+    diagnostic flag but does not route (a mostly-raster page with a clean text
+    layer stays ``fast``);
   * the text-quality score distinguishes clean prose from mashed/space-less junk.
 """
 
@@ -236,12 +238,16 @@ def test_quality_floor_override_disables_trigger():
     assert c.recommended_tier == "fast"
 
 
-def test_scan_signal_routes_ocr_even_with_clean_text():
+def test_image_heavy_clean_text_stays_fast():
-    # clean text but every page is a raster scan -> OCR (the Student-147 case)
+    # Image coverage is diagnostic, not routing: fully-raster pages whose text
+    # layer is already clean (a scan carrying a good OCR layer, or a figure-heavy
+    # digital page) carry the image_heavy flag but stay on the fast tier --
+    # re-OCR adds nothing. This was the ~45% over-escalation on OHR-Bench.
     full, bounds = _two_page(_CLEAN, _CLEAN)
     c = clf.classify_from_text(full, bounds, image_coverage=[1.0, 1.0])
-    assert c.recommended_tier == "ocr"
+    assert c.recommended_tier == "fast"
     assert "image_heavy" in c.flags
+    assert all(p.needs_ocr is False for p in c.pages)
 
 
 def test_scan_signal_ignored_when_coverage_low():
@@ -270,9 +276,10 @@ def test_image_coverage_per_page():
     assert len(digital) == 2 and all(c < 0.1 for c in digital)
 
 
-def test_scan_coverage_shorter_than_pages_falls_back_to_text():
+def test_scan_coverage_shorter_than_pages_aligns_without_crash():
     # image_coverage shorter than the boundaries (the MAX_SAMPLED_PAGES cap):
-    # page 0 is flagged scanned; later pages fall back to the text-quality signal.
+    # the single entry aligns to page 0; later pages get no coverage entry. With
+    # clean text everywhere, routing is on text only, so nothing escalates.
     n = len(_CLEAN)
     full = _CLEAN * 3
     bounds = [
@@ -281,6 +288,8 @@ def test_scan_coverage_shorter_than_pages_falls_back_to_text():
         {"page": 3, "start_offset": 2 * n, "end_offset": 3 * n},
     ]
     c = clf.classify_from_text(full, bounds, image_coverage=[1.0])
-    assert c.pages[0].needs_ocr is True  # scanned (coverage)
+    assert c.pages[0].image_coverage == pytest.approx(1.0)  # entry aligned
-    assert c.pages[1].needs_ocr is False  # clean text, no coverage entry
+    assert c.pages[1].image_coverage == pytest.approx(0.0)  # no entry -> 0
-    assert c.recommended_tier == "fast"  # only 1/3 pages bad
+    assert all(p.needs_ocr is False for p in c.pages)  # coverage no longer routes
+    assert "image_heavy" in c.flags  # but page 0 still flags image_heavy
+    assert c.recommended_tier == "fast"