feat: Symbole zentralisieren + Fragment-Ueberschriften filtern

Perceptual-Hash-basierte Symbol-/Duplikaterkennung in lib/pdf_extract.py:

- average_hash() (aHash) + Hamming-Distanz statt exaktem Byte-Hash: erkennt
  visuell gleiche Bilder trotz unterschiedlicher Skalierung/Kompression
- Symbole (klein <=260px UND mehrfach vorkommend, z.B. Warndreiecke) werden
  EINMAL zentral nach assets/shared/symbols/symbol_<hash>.png abgelegt und im
  RST als .. image:: (12mm, zentriert) auf die eine Quelle referenziert -
  dokumentuebergreifend (registry). Ein Warndreieck: 1x gespeichert statt 58x
- grossformatige Inhaltsbild-Duplikate ebenfalls ueber aHash zusammengefuehrt
- Fragment-"Ueberschriften" (POS, li., re., "bar, max. 10 bar") unterdrueckt
- Altbestand assets/shared/symbols/img_*.png (durcheinander benannt) entfernt

Wirkung: omniflo images/ 83 -> 62, 14 zentrale Symbole statt zig Duplikate.
Voller PDF-Build sauber (lualatex exit 0, 719 Seiten).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Michael Stangl
2026-09-23 14:10:33 +02:00
co-authored by Claude Opus 4.8
parent c912b1e9d5
commit bbfe17ec37
31 changed files with 173 additions and 24 deletions
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