Run detect_graphic_elements() in the grid pipeline after image loading
and remove ALL words whose centroids fall inside detected graphic regions,
regardless of confidence. Previously only low-confidence words (conf < 50)
were removed, letting artifacts like "Tr", "Su" survive.
Changes:
- grid_editor_api.py: Import and call detect_graphic_elements() at Step 3a,
passing only significant words (len >= 3) to avoid short artifacts fooling
the text-vs-graphic heuristic. Hard-filter all words in graphic regions.
- cv_graphic_detect.py: Lower density threshold from 20% to 5% for large
regions (>100x80px) — photos/illustrations have low color saturation.
Raise page-spanning limit from 50% to 60% width/height.
Tested: 5 ground-truth sessions pass regression (079cd0d9, d8533a2c,
2838c7a7, 4233d7e3, 5997b635). Session 5997 now detects 2 graphic regions
and removes 29 artifact words including "Tr" and "Su".
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The 25x25 dilation kernel merges nearby green words into large regions,
so pixel-overlap with OCR word boxes drops below 50%. Previous density
checks alone weren't sufficient.
New multi-layered approach:
- Count OCR word CENTROIDS inside each colored region
- ≥2 centroids → definitely text (images don't produce multiple words)
- 1 centroid + 10%+ pixel overlap → likely text
- Lower pixel overlap threshold from 50% to 40%
- Raise density+height thresholds for text-line detection
- Use INFO logging to diagnose remaining false positives
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add color pixel density checks to cv_graphic_detect.py Pass 1:
- density < 20% → skip (text strokes are thin, images are filled)
- density < 30% + height < 4% page → skip (colored text line)
This fixes green headings (Insel, Internet, Inuit) being removed
as graphic regions, which also caused word reordering in lines.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New approach: dilate color mask heavily (25x25) to merge nearby colored
pixels into regions, then check word overlap:
- >50% overlap with OCR word boxes → colored text → skip
- <50% overlap → colored image/graphic → keep
This detects balloon clusters as one "image" region instead of trying
to classify individual shapes. Red words like "borrow/lend" are filtered
because they overlap with their word boxes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The 5x5 MORPH_CLOSE was connecting scattered color pixels into one
page-spanning contour that swallowed individual balloons. Fix:
- Remove MORPH_CLOSE, keep only MORPH_OPEN for speckle removal
- Lower sat threshold 50→40 to catch more colored elements
- Filter contours spanning >50% of width OR height (was AND)
- Filter contours >10% of image area
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pass 1 (color): Detect colored graphics on HSV saturation channel.
Black text is invisible on this channel, so no word exclusion needed.
Catches colored balloons, arrows, icons reliably.
Pass 2 (ink): Detect large black illustrations on dark ink mask
minus word exclusion. Only keeps area > 5000 to avoid text fragments.
Fixes: all 5 balloons now detectable (previously word exclusion zones
were eating colored graphics that overlapped with nearby OCR words).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Text fragments after word exclusion are indistinguishable from arrows
and icons via contour metrics. Since the goal is detecting graphics,
images, boxes and colors (not arrows/icons), simplify to only:
- circle/balloon (circularity > 0.55 — very reliable)
- illustration (area > 3000 — clearly non-text)
Boxes and colors are handled by cv_box_detect and cv_color_detect.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Lower min_area from 200 to 80 (small balloons ~100-300px²)
- Lower word_pad from 10 to 5 (10px was eating nearby graphics)
- Relax circle detection: circularity>0.55, min_dim>15 (was 0.70/25)
- Text fragments still filtered by _classify_shape noise threshold
- Add ACCEPT logging for debugging
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Raise min_area from 30 to 200 (text fragments are small)
- Raise word_pad from 3 to 10px (OCR bboxes are tight)
- Reduce morph close kernel from 5x5 to 3x3 (avoid reconnecting text)
- Tighten arrow detection: min 20px, circularity<0.35, >=2 defects
- Add 'noise' category for too-small elements, filter them out
- Raise min dimension from 4 to 8px
- Add debug logging for word count and exclusion coverage
- Raise max_area_ratio to 0.25 (allow larger illustrations)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add cv_graphic_detect.py for detecting non-text visual elements (arrows,
circles, lines, exclamation marks, icons, illustrations). Draw detected
graphics on structure overlay image and display them in the frontend
StepStructureDetection component with shape counts and individual listings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
