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, Node.js, and browser WebAssembly.

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.

• 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.
• Browser WebAssembly — Run the same Rust parser locally in browsers and Web Workers, with embedded CMaps and no server round trip.
• Lightweight — Pure Rust, no ML models, no external services. Single dependency on lopdf for PDF parsing.

Evaluated on the opendataloader-bench corpus (200 PDFs). Only local engines without model-based PDF parsing are shown; OCR was disabled. Scores are 0-1, higher is better.

Results were refreshed on July 31, 2026, on an Apple M4 Pro. Engine versions were pdf-inspector 0.2.6, LiteParse 2.10.1, OpenDataLoader 2.2.1, PyMuPDF4LLM 0.2.0, and MarkItDown 0.1.5. Speed is the median of five alternating or rotating complete corpus runs after an excluded warm-up run, with each parser processing documents sequentially in a single process.

The complete parser configuration, per-document predictions, evaluator output, and generated charts are available in the reproducible results branch.

Best fit: Native-text PDFs where speed, reading order, and table structure matter. In this comparison, pdf-inspector delivered the higher overall, reading-order, and table scores, along with the fastest complete run. That makes it a strong local default for reports, research papers, financial documents, invoices, and legal PDFs that need clean, structured Markdown without adding OCR latency or infrastructure.

Use the paired benchmark harness to compare two local builds against the exact same corpus and evaluator revision.

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

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

npm install @firecrawl/pdf-inspector-wasm

import init, { processPdf } from '@firecrawl/pdf-inspector-wasm';

await init();
const response = await fetch('/document.pdf');
const pdf = new Uint8Array(await response.arrayBuffer());
const result = processPdf(pdf);

console.log(result.pdfType);
console.log(result.markdown);

Full API reference: wasm/README.md

Install from crates.io:

cargo add pdf-inspector

Or add it manually:

[dependencies]
pdf-inspector = "0.1"

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

# Install the CLI tools
cargo install pdf-inspector

# Convert PDF to Markdown
pdf2md document.pdf

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

# Positioned TextItem JSON, including is_underline metadata
pdf2md document.pdf --items-json

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

# Token-efficient output (collapses long dot leaders and similar source padding)
pdf2md document.pdf --compact

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

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

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

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

From a source checkout, use cargo run --bin pdf2md -- document.pdf or cargo run --bin detect-pdf -- document.pdf instead.

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.

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)
wasm/                   — Browser bindings (wasm-bindgen)

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.

The converter handles:

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).

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

MIT