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A PDF text extraction library that gets the hard parts right.
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jedarden c2870e6640 Add research docs and SDK invocation notes 
Four research documents covering PDF spec fundamentals, font types and
encoding, glyph Unicode recovery, and tagged PDF structure/reading order.
SDK invocation notes with subprocess and HTTP examples for Python, Node.js,
Go, Ruby, Java, Rust, and Bash.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-16 14:33:34 -04:00
docs Add research docs and SDK invocation notes 2026-05-16 14:33:34 -04:00
README.md Initial repo scaffold with README and docs structure 2026-05-16 14:26:16 -04:00

README.md

pdftract

A PDF text extraction library designed to address the persistent shortcomings of existing tools.

The problem

Current PDF text extractors — PyMuPDF, pdfplumber, pdfminer, Camelot, Tabula, marker, nougat — cover a lot of ground but share a set of well-known, largely unsolved failures:

  • Reading order is broken for multi-column layouts, sidebars, footnotes, and mixed-layout pages. Most tools dump text in PDF operator order or naive top-to-bottom order.
  • Font encoding failures produce silent garbage when PDFs use missing or incorrect ToUnicode CMaps, Type3 fonts, or symbol-font abuse for math.
  • Tagged PDFs are ignored. PDF/UA and PDF/A documents contain a StructTree with explicit logical structure — headings, paragraphs, lists, tables, reading order — that almost no extractor reads.
  • No confidence or provenance. Extracted text carries no signal about reliability, bounding box, or font metadata, making downstream filtering and validation impossible.
  • Hybrid documents are mishandled. PDFs that mix vector pages and scanned pages are treated as one type throughout, degrading accuracy on both.
  • Flat output. Nearly every tool returns a string or character stream. RAG pipelines, LLM preprocessing, and document QA need structured output — sections, headings, tables, figures — not a flat dump.

What pdftract does differently

  • Reads StructTree when present (PDF/UA, PDF/A) for near-perfect logical structure at zero cost
  • Per-page hybrid routing: each page is independently classified and sent to the right pipeline (vector extraction, full OCR, or assisted OCR where vector text hints improve accuracy)
  • Font encoding recovery via glyph fingerprinting to reconstruct correct Unicode mappings
  • Layout region segmentation for reading order without requiring a full neural OCR pipeline
  • Structured JSON output as the primary interface, with per-span bounding box and confidence score

Architecture

Rust core with PyO3 Python bindings and a CLI binary. The binary can run as a microservice (pdftract serve) for container deployments — the container is just the binary in serve mode, not a separate product.

pdftract extract invoice.pdf          # stdout JSON
pdftract extract invoice.pdf --text   # plain text
pdftract serve --port 8080            # HTTP: POST /extract

Status

Early development. See docs/plan/ for the implementation roadmap and docs/research/ for analysis of existing tools and approaches.