docs: list docling, PaddleOCR, DeepSeek-OCR, Qwen3-VL-30B-A3B for OCR-engine evaluation

User-requested candidates to install as Docker containers alongside the
existing engines and add through the Engines tab, to be benchmarked
against the two already wired (baidu/Unlimited-OCR, KDL-Frontier-Parser-
nano) for both page-text transcription and open-ended figure/chart
description. No code change.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

TODO.md

@@ -416,6 +416,24 @@ casting. The original PDF remains the visual source of truth while approved arti
 - [~] Add OCR-engine benchmark comparisons and per-page fallback selection.
       *(per-page fallback done, now a real two-engine chain: vision OCR escalates when a page needs OCR or scores
       poorly, KDL Frontier Parser nano is the vision fallback, tesseract is the last resort; no formal benchmark)*
+- [ ] **Evaluate additional OCR/vision candidates against the two already wired (baidu/Unlimited-OCR,
+      KDL-Frontier-Parser-nano) and pick the best fit per task** — user-requested 2026-09-14. Goal: low RAM/VRAM
+      footprint with excellent speed and quality, for both page-text transcription and open-ended figure/chart
+      description (the latter is the harder requirement — confirmed live that baidu/Unlimited-OCR is
+      transcription-only and cannot do it at all, see `architecture/magazine-ingestion.md` and CHANGELOG 1.29.0).
+      Candidates to install as Docker containers alongside the existing engines, each added through the Engines tab
+      (`routes/docker.py`'s `_LOCAL_CONTAINER_DEFS`, role `ocr`) so they're selectable/connectable/benchmarkable like
+      any other engine, not hardcoded:
+      - [ ] [docling](https://github.com/docling-project/docling) — document conversion/layout toolkit.
+      - [ ] [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) — established general-purpose OCR.
+      - [ ] [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) — vision-language OCR model.
+      - [ ] Qwen3-VL-30B-A3B-Instruct-FP8 — general vision-language model; check whether it fits the RAM/VRAM
+            budget at 30B-A3B FP8 (likely the heaviest candidate — weigh against the "low footprint" goal
+            specifically) and whether it's meaningfully better at open-ended description than the nano-class
+            candidates above.
+      For each: measure page-transcription accuracy and figure/chart-description quality against the existing
+      c't KI Wissen 2026 test corpus, plus idle/active VRAM footprint and latency, before deciding what replaces or
+      joins the current primary/fallback pair.
 
 ### P2 — Finding magazines to read