A PDF text extraction library that gets the hard parts right.
8b5dd4febb
This commit creates user-facing documentation for each built-in profile: - Profile YAML files defining match criteria, priority, and extracted fields - Per-profile READMEs with match criteria summary, extracted fields table, known limitations, sample input pointers, and configuration tips - xtask skeleton generator for automated README generation Profiles documented: - invoice: Commercial invoices with line items, vendor/customer, totals - receipt: POS receipts with items, payment method - contract: Legal contracts with parties, effective date, term, signatures - scientific_paper: Academic papers with title, authors, abstract, DOI, references - slide_deck: Presentation slides with title, presenter, date, slide titles - form: Fillable forms (degenerate case: uses Phase 7.4 form_fields) - bank_statement: Bank statements with account info, period, balances, transactions - legal_filing: Court filings with case number, court, parties, filing date, docket - book_chapter: Book chapters with title, chapter number, author, section headings Closes: pdftract-4iier Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| notes | ||
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| xtask | ||
| Cargo.lock | ||
| Cargo.toml | ||
| README.md | ||
pdftract
A PDF text extraction library that gets the hard parts right.
What it does
- Correct reading order — layout regions are segmented and sequenced before text is emitted, handling multi-column pages, sidebars, footnotes, and mixed-layout documents without relying on PDF operator order
- Font encoding recovery — when
ToUnicodeCMaps are absent, wrong, or incomplete, pdftract works through a layered recovery pipeline: glyph name lookup via the Adobe Glyph List, font fingerprinting against known metrics and embedded checksums, and glyph outline shape matching - Structure tree extraction — PDF/UA and PDF/A documents encode their logical structure (headings, paragraphs, lists, tables, reading order) in a
StructTree; pdftract reads this directly when present, producing accurate semantic output at no extra cost - Per-page hybrid routing — each page is independently classified and routed to the appropriate pipeline: vector text extraction, full OCR, or assisted OCR where vector hints improve raster accuracy
- Structured output with provenance — the primary output is JSON carrying per-span bounding boxes, font name, size, and confidence score alongside the extracted text, not a flat string dump
Output
{
"pages": [
{
"page": 1,
"blocks": [
{ "kind": "heading", "text": "Introduction", "bbox": [72, 680, 400, 700] },
{ "kind": "paragraph", "text": "...", "bbox": [72, 640, 540, 670] }
],
"spans": [
{ "text": "Introduction", "bbox": [72, 680, 400, 700], "font": "Times-Bold", "size": 14.0, "confidence": 0.99 }
]
}
],
"metadata": { "title": "...", "author": "...", "page_count": 10 }
}
Usage
pdftract extract invoice.pdf # structured JSON to stdout
pdftract extract invoice.pdf --text # plain text to stdout
pdftract extract invoice.pdf --output out.json
pdftract serve --port 8080 # HTTP service: POST /extract
Architecture
Rust core with PyO3 Python bindings and a CLI binary. The same binary runs as a command-line tool or as an HTTP microservice — the container deployment is just pdftract serve.
See docs/research/ for technical deep-dives into the PDF specification, font encoding, glyph Unicode recovery, and tagged PDF structure. See docs/notes/ for SDK invocation examples in Python, Node.js, Go, Ruby, Java, Rust, and Bash.
Status
Early development. See docs/plan/ for the implementation roadmap.