pdf-inspector

Fast Rust library for PDF classification and text extraction. Detects whether a PDF is text-based or scanned, extracts text with position awareness, and converts to clean Markdown — all without OCR. Includes bindings for Python and Node.js.

Built by Firecrawl to handle text-based PDFs locally in under 200ms, skipping expensive OCR services for the ~54% of PDFs that don't need them.

Features

• Smart classification — Detect TextBased, Scanned, ImageBased, or Mixed PDFs in ~10-50ms by sampling content streams. Returns a confidence score (0.0-1.0) and per-page OCR routing.
• Text extraction — Position-aware extraction with font info, X/Y coordinates, and automatic multi-column reading order.
• Markdown conversion — Headings (H1-H4 via font size ratios), bullet/numbered/letter lists, code blocks (monospace font detection), tables (rectangle-based and heuristic), bold/italic formatting, URL linking, and page breaks.
• Table detection — Dual-mode: rectangle-based detection from PDF drawing ops, plus heuristic detection from text alignment. Handles financial tables, footnotes, and continuation tables across pages.
• CID font support — ToUnicode CMap decoding for Type0/Identity-H fonts, UTF-16BE, UTF-8, and Latin-1 encodings.
• Multi-column layout — Automatic detection of newspaper-style columns, sequential reading order, and RTL text support.
• Encoding issue detection — Automatically flags broken font encodings so callers can fall back to OCR.
• Single document load — The document is parsed once and shared between detection and extraction, avoiding redundant I/O.
• Lightweight — Pure Rust, no ML models, no external services. Single dependency on lopdf for PDF parsing.

Benchmark

Evaluated on the opendataloader-bench corpus (200 PDFs). Only direct text extraction engines are shown — no OCR, no ML models. Scores are 0-1, higher is better.

Engine	Overall	Reading Order (NID)	Tables (TEDS)	Headings (MHS)	Speed (200 docs)
pdf-inspector	0.78	0.87	0.59	0.57	4s
opendataloader	0.84	0.91	0.49	0.74	11s
pymupdf4llm	0.73	0.89	0.40	0.41	18s
markitdown	0.58	0.88	0.00	0.00	8s

For context, engines that use OCR/ML (docling, marker, mineru) score 0.83-0.88 overall but take 2-180 minutes on the same corpus.

Where we do well: Speed (fastest of all engines), reading order, table detection vs other direct-text tools.

Where we lag: Heading detection trails opendataloader — many PDFs use bold text at body font size for headings, or headings that are only slightly larger than body text. Table detection trails OCR-based engines that can see visual table structure.

Quick start

Python

pip install maturin
maturin develop --release

import pdf_inspector

result = pdf_inspector.process_pdf("document.pdf")
print(result.pdf_type)   # "text_based", "scanned", "image_based", "mixed"
print(result.markdown)   # Markdown string or None

Full API reference: docs/python.md

Node.js

npm install @firecrawl/pdf-inspector

import { readFileSync } from 'fs';
import { processPdf, classifyPdf } from '@firecrawl/pdf-inspector';

const result = processPdf(readFileSync('document.pdf'));
console.log(result.pdfType);   // "TextBased", "Scanned", "ImageBased", "Mixed"
console.log(result.markdown);  // Markdown string or null

Full API reference: napi/README.md

Rust

[dependencies]
pdf-inspector = { git = "https://github.com/firecrawl/pdf-inspector" }

use pdf_inspector::process_pdf;

let result = process_pdf("document.pdf")?;
println!("Type: {:?}", result.pdf_type);
if let Some(markdown) = &result.markdown {
    println!("{}", markdown);
}

Full API reference: docs/rust-api.md

CLI

# Convert PDF to Markdown
cargo run --bin pdf2md -- document.pdf

# JSON output (for piping)
cargo run --bin pdf2md -- document.pdf --json

# Raw markdown only (no headers)
cargo run --bin pdf2md -- document.pdf --raw

# Insert page break markers (<!-- Page N -->)
cargo run --bin pdf2md -- document.pdf --pages

# Process only specific pages
cargo run --bin pdf2md -- document.pdf --select-pages 1,3,5-10

# Detection only (no extraction)
cargo run --bin detect-pdf -- document.pdf
cargo run --bin detect-pdf -- document.pdf --json

# Detection + layout analysis (tables, columns)
cargo run --bin detect-pdf -- document.pdf --analyze --json

Architecture

PDF bytes
  │
  ├─► detector         → PdfType (TextBased / Scanned / ImageBased / Mixed)
  │
  └─► extractor
        ├─ fonts        → font widths, encodings
        ├─ content_stream → walk PDF operators → TextItems + PdfRects
        ├─ xobjects     → Form XObject text, image placeholders
        ├─ links        → hyperlinks, AcroForm fields
        └─ layout       → column detection → line grouping → reading order
              │
              ├─► tables
              │     ├─ detect_rects      → rectangle-based tables (union-find)
              │     ├─ detect_heuristic  → alignment-based tables
              │     ├─ grid              → column/row assignment → cells
              │     └─ format            → cells → Markdown table
              │
              └─► markdown
                    ├─ analysis     → font stats, heading tiers
                    ├─ preprocess   → merge headings, drop caps
                    ├─ convert      → line loop + table/image insertion
                    ├─ classify     → captions, lists, code
                    └─ postprocess  → cleanup → final Markdown

The document is loaded once via load_document_from_path / load_document_from_mem and shared between the detection and extraction stages, so there's no redundant parsing.

Project structure

src/
  lib.rs                — Public API, PdfOptions builder, convenience functions
  python.rs             — PyO3 Python bindings
  types.rs              — Shared types: TextItem, TextLine, PdfRect, ItemType
  text_utils.rs         — Character/text helpers (CJK, RTL, ligatures, bold/italic)
  process_mode.rs       — ProcessMode enum (DetectOnly, Analyze, Full)
  detector.rs           — Fast PDF type detection without full document load
  glyph_names.rs        — Adobe Glyph List → Unicode mapping
  tounicode.rs          — ToUnicode CMap parsing for CID-encoded text
  extractor/            — Text extraction pipeline
  tables/               — Table detection and formatting
  markdown/             — Markdown conversion and structure detection
  bin/                  — CLI tools (pdf2md, detect_pdf)
napi/                   — Node.js/Bun bindings (napi-rs)

How classification works

1. Parse the xref table and page tree (no full object load)
2. Select pages based on ScanStrategy (default: all pages with early exit)
3. Look for Tj/TJ (text operators) and Do (image operators) in content streams
4. Classify based on text operator presence across sampled pages

This detects 300+ page PDFs in milliseconds. The result includes pages_needing_ocr — a list of specific page numbers that lack text, enabling per-page OCR routing instead of all-or-nothing.

Scan strategies

Strategy	Behavior	Best for
EarlyExit (default)	Scan all pages, stop on first non-text page	Pipelines routing TextBased PDFs to fast extraction
Full	Scan all pages, no early exit	Accurate Mixed vs Scanned classification
Sample(n)	Sample n evenly distributed pages (first, last, middle)	Very large PDFs where speed matters more than precision
Pages(vec)	Only scan specific 1-indexed page numbers	When the caller knows which pages to check

Markdown output

The converter handles:

Element	How it's detected
Headings (H1-H4)	Font size tiers relative to body text, with 0.5pt clustering
Bold/italic	Font name patterns (Bold, Italic, Oblique)
Bullet lists	*, -, *, ○, ●, ◦ prefixes
Numbered lists	1., 1), (1) patterns
Letter lists	a., a), (a) patterns
Code blocks	Monospace fonts (Courier, Consolas, Monaco, Menlo, Fira Code, JetBrains Mono) and keyword detection
Tables	Rectangle-based detection from PDF drawing ops + heuristic detection from text alignment
Financial tables	Token splitting for consolidated numeric values
Captions	"Figure", "Table", "Source:" prefix detection
Sub/superscript	Font size and Y-offset relative to baseline
URLs	Converted to Markdown links
Hyphenation	Rejoins words broken across lines
Page numbers	Filtered from output
Drop caps	Large initial letters merged with following text
Dot leaders	TOC-style dots collapsed to " ... "

Use case: smart PDF routing

pdf-inspector was built for pipelines that process PDFs at scale. Instead of sending every PDF through OCR:

PDF arrives
  → pdf-inspector classifies it (~20ms)
  → TextBased + high confidence?
      YES → extract locally (~150ms), done
      NO  → send to OCR service (2-10s)

This saves cost and latency for the majority of PDFs that are already text-based (reports, papers, invoices, legal docs).

Debugging

See docs/debugging.md for RUST_LOG environment variable usage.

License

MIT