A PDF text extraction library that gets the hard parts right.
a3178a3960
Add tests/sdk-conformance/ containing the shared, language-neutral test specification for all pdftract SDKs. The suite includes 32 cases covering all 9 contract methods (extract, extract_text, extract_markdown, extract_stream, search, get_metadata, hash, classify, verify_receipt) across vector, scanned, encrypted, fillable-form, mixed, large, broken, and remote PDFs. - cases.json: 32 test cases with id, fixture, method, options, expected, tolerances, feature tags, and min_schema_version - schema.json: JSON Schema v7 draft for validating test case structure - validate_suite.py: Validation script that checks structure and fixture existence - fixtures/: Test PDFs organized by category (symlinks to classifier fixtures for shared files) See notes/pdftract-1527.md for verification details. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
||
|---|---|---|
| .ci/argo-workflows | ||
| .git-hooks | ||
| crates/pdftract-core | ||
| docs | ||
| notes | ||
| profiles/builtin | ||
| scripts | ||
| src | ||
| tests | ||
| xtask | ||
| .needle-predispatch-sha | ||
| Cargo.lock | ||
| Cargo.toml | ||
| CLAUDE.md | ||
| mod | ||
| 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.