A PDF text extraction library that gets the hard parts right.
d161d109b3
- Add Primary Objectives section with CI-gated measurable targets: accuracy (CER <0.5%, WER <3%, readability >0.85), speed (100pp <3s, 10x vs pdfminer), weight (<4MB default binary, <20 default deps) - Add feature-flag strategy: axum/tokio/pdfium/pyo3 are all optional; default build is core extraction + CLI only - Add Phase 4.7: text readability validation and correction pipeline (ligature repair, hyphenation, mojibake detection, readability scoring) - Make pdfium-render explicitly optional (full-render feature) vs. the always-present direct image compositing path - Add Tier 4 competitive benchmark suite (vs. pdfminer.six, pypdf, pdfplumber) - Remove jpeg-decoder and whichlang from dependency matrix (unnecessary) - Rename implementation-plan.md → plan.md (matches CLAUDE.md reference) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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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.