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 , 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.
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.
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.
Benchmark
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.
Quick start
Python
Full API reference: docs/python.md
Node.js
Full API reference: napi/README.md
Browser WebAssembly
Full API reference: wasm/README.md
Rust
Install from crates.io :
Or add it manually:
Full API reference: docs/rust-api.md
CLI
From a source checkout, use cargo run --bin pdf2md -- document.pdf or cargo run --bin detect-pdf -- document.pdf instead.
Architecture
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
How classification works
Parse the xref table and page tree (no full object load)
Select pages based on ScanStrategy (default: all pages with early exit)
Look for Tj / TJ (text operators) and Do (image operators) in content streams
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
Markdown output
The converter handles:
Use case: smart PDF routing
pdf-inspector was built for pipelines that process PDFs at scale. Instead of sending every PDF through OCR:
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