centralia
Court PDF opinion extractor: a PDF plus a court id in, a typed document out.
Requires Python 3.12 or newer.
python -m pip install centralia
from centralia import read
r = read("opinion.pdf", court_id="nmariana") # path, bytes, or file object
r["status"] # valid | review | scanned | failed
r["cluster"] # the case: citation, docket, dates (as printed + ISO), panel, parties
r["opinions"] # one entry per writing, each with its author, pages, html and text
r["diagnostics"] # page facts and anything unplaced — reported, never judged
r["html"] # the document's text, without review furniture
r["review_html"] # the reviewer's page: criteria box, Removed panel, role tints
r["casebody"] # Harvard casebody XML
Lower level, for callers that want the objects:
from centralia import extract, render_opinion
result = extract("opinion.pdf", court_id="mont")
result.document # typed Document
result.trace # per-decision evidence chains
render_opinion(result.document.opinions[0])
Dates are returned twice: as the court printed them (date_filed) and as
YYYY-MM-DD where that can be read without guessing (date_filed_iso, or
None). diagnostics["dates_unparsed"] names the ones that did not parse.
status is a report, not a gate. diagnostics carries the page-level facts a
caller needs to decide for itself: which pages are a raster (scan_pages),
which pages have no text layer at all so their words are absent
(text_missing_pages), which carry unmapped glyphs (cid_pages), and what
the extractor could not place (residual).
1 maintainer
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