Ship the first end-to-end cut of the pdfsys pipeline on OmniDocBench-100:
* pdfsys-router: port FinePDFs PDFFeatureExtractor (15 page features × 8
sampled pages + 4 doc features = 124 columns) and load the upstream
xgb.ubj weights via a thin XgbRouterModel wrapper. Router.classify()
returns a RouterDecision with Backend {MUPDF, PIPELINE, VLM, DEFERRED},
ocr_prob, and the full feature dict for debugging. Seeded RNG keeps the
feature vector reproducible per PDF. Weights live under models/ and are
gitignored; download_weights.py fetches them from the HF LFS media URL.
* pdfsys-parser-mupdf: text-ok backend using page.get_text("blocks",
sort=True), emits one Segment per paragraph-shaped block with bbox
normalized to [0, 1] and converts the whole doc into an ExtractedDoc
with merged Markdown. No layout-analyser dependency by design.
* pdfsys-bench: add quality.py (ModernBERT-large regression head from
HuggingFaceFW/finepdfs_ocr_quality_classifier_eng_Latn, loaded in
…
chore: bootstrap pdfsys workspace with 7 package skeletons
