* feat(npm): split platform binaries into optionalDependencies (1.11.0)
The single package bundled all three .node binaries (17.6 MB unpacked)
so every install downloaded every platform. Publish one package per
platform (@firecrawl/pdf-inspector-{linux-x64-gnu,darwin-arm64,
win32-x64-msvc}) holding just its binary; the napi-generated loader
already falls back to exactly these names. Main package drops *.node
from files (8.5 kB tarball) and pins the platform packages as
optionalDependencies, re-stamped to the exact version at publish time.
Publish workflow gains a workflow_dispatch fallback and per-package
already-published checks so partial releases can be retried.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs(npm): document Windows support and platform packages; drop stale napi.package.name
Review follow-ups: the README claimed only linux-x64 and macOS ARM64
despite the win32-x64-msvc binary shipping, and napi.package.name
(@firecrawl/pdf-inspector-js) contradicts the real platform package
prefix — the loader and workflow derive it from the root package name.
Verified the generated loader is unchanged without the config.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* fix(lib): detect substitution-cipher garbled text from broken ToUnicode CMaps
ParseBench text_simple__att10k.pdf (issue #118) ships Type0/Identity-H
fonts whose ToUnicode CMaps are authored garbled: every bfrange maps with
a wrong constant delta, so text extracts as pure-ASCII ciphertext
("Certificate" -> "8VceZWZTReV"). The embedded subset font has no cmap
table and no glyph names, so no decode source can recover the real text
(poppler and mupdf emit the same ciphertext). The only correct behavior
is to flag the page for OCR instead of serving the garbage silently --
but the text is 100% printable ASCII with word-like tokens, so it slipped
past is_garbage_text and detect_encoding_issues.
Add CipherGarbleStats, a letter-statistics discriminator that flags a
Latin-dominant sample (>=200 ASCII letters) when vowels are starved
(<=30% of letters) AND either:
- lowercase->uppercase transitions inside words exceed 10% of letter
bigrams (a shifted lowercase alphabet straddles the ASCII uppercase
block), or
- the letter histogram's cosine similarity against English letter
frequencies drops below 0.60 (catches shifts that stay within case
blocks).
Wired into analyze_text_quality (per-page, item-level) and
detect_encoding_issues (markdown-level), so extract_pages_markdown
reports needs_ocr + suspected_garbled_text and suppresses the garbage.
Thresholds validated against the 380-document pdf-evals snapshot corpus
(Swedish, Finnish, Turkish, German, romaji, schematics, all-caps and
camelCase-heavy docs): zero false positives, and byte-identical eval
output vs main. Garbled page measures vowel ratio 0.245 / case-shift
rate 0.225 / cosine 0.532; closest legitimate document on each axis is
0.264 / 0.021 / 0.801.
Fixes#118
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chore: bump pdf-inspector to 0.1.4, npm package to 1.9.11
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(lib): exempt uniform-case structured content from cipher detection
Address PR review (cubic P2): the frequency branch (english_cosine < 0.60)
fired on any Latin-dominant, low-vowel letter distribution unlike English,
so non-linguistic ASCII — DNA/protein sequences, ticker symbols, hex dumps —
could be suppressed and routed to OCR despite not being garbled. Measured:
DNA cosine 0.428 / vowel ratio 0.260, protein 0.738, tickers 0.747, hex
0.549 — all would have flagged.
Add a mixed-case guard to looks_garbled: garbled English is a permutation of
natural language and carries sentence capitalization (block-straddling shifts
invert the ratio — att10k is 60% uppercase; in-case Caesar shifts preserve it
at ~3%), so both keep some of each case. The exempted structured content is
uniform case (all upper or all lower). Requiring the minority case to be >=1%
of ASCII letters exempts single-case sequences while preserving both garble
signals, including the in-case-shift scenario the frequency branch exists for.
Strictly tightens the detector: it can only remove flags, so the eval corpus
stays at zero false positives (verified byte-identical to a baseline main
binary across all 185 PDFs) and att10k remains flagged. Adds regression tests
for DNA, protein, tickers, and an in-case Caesar shift.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(lib): make cipher detection case-agnostic via sorted-histogram shape
Address PR review follow-up: the mixed-case guard from the previous commit
returned before the vowel/frequency checks, creating a blind spot — a
uniform-case (all-lower or all-upper) substitution cipher is a plausible
broken-CMap output and would bypass OCR entirely.
Replace the case proxy with the actual invariant. A substitution cipher is
a bijection over a real language's alphabet, so it preserves the frequency
SHAPE (the sorted histogram) while scrambling letter POSITIONS (the unsorted
histogram). Signal 2 now flags when english_cosine < 0.60 (positions unlike
English) AND english_shape_cosine >= 0.90 (profile is still English-shaped).
This is independent of case, so it catches all-lower, all-upper, and
case-straddling shifts alike.
The exempted structured content fails one half: DNA/hex dumps have too steep
a profile (shape cosine 0.74 / 0.81 < 0.90), while protein sequences, ticker
symbols and base64 are not sufficiently unlike English in position (unsorted
cosine 0.74 / 0.75 / 0.77 >= 0.60). All stay out of OCR.
Still strictly corpus-safe: every real Latin document scores unsorted cosine
>= 0.70 (min 0.80), far above the 0.60 gate, so none can reach Signal 2.
Re-verified byte-identical to a baseline main binary across all 185 eval
PDFs; att10k remains flagged. Drops the now-unused case counters and adds
all-lowercase / all-uppercase shifted-prose regression tests.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chore: source Python package version from Cargo.toml via maturin
Address PR review (cubic P2): pyproject.toml pinned version = "0.1.0",
which overrides Cargo.toml, so a maturin build produced a 0.1.0 Python
artifact regardless of the crate version (it had drifted since the PyO3
bindings were added). Switch to dynamic = ["version"] so maturin sources
the version from Cargo.toml [package] version and the two can no longer
diverge. No workflow auto-publishes the Python package, so this is metadata
hygiene rather than a release-path fix.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
When fire-pdf sends pre-segmented bboxes from the layout model,
pdf-inspector no longer runs column detection, stream-order heuristics,
or newspaper/tabular mode detection within the region. These heuristics
conflict with the layout model's decisions and cause wrong reading order.
Region extraction now simply: Y-sorts items, groups into lines, and
sorts within each line by X position. The heavy heuristics remain
available for standalone full-page extraction.
Eval showed pure OCR (0.2875 NED) beating native+heuristics (0.2916)
across all categories, especially multi-column (-0.08) and newspaper
(-0.16). This change should close that gap.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Move the napi bridge from fire-pdf into pdf-inspector as `napi/`.
Package name: @firecrawl/pdf-inspector-js, published to GitHub Packages
(npm.pkg.github.com) as a public package on v* tags.
Exposes two functions:
- classifyPdf(buffer) → type, page count, pages needing OCR
- extractTextInRegions(buffer, pageRegions) → per-region text with
needsOcr quality flag (GID fonts, garbage, encoding issues)
Includes publish workflow that builds linux-x64-gnu + darwin-arm64
binaries and publishes main + platform-specific packages.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
