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>
