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pdf-inspector
pdf-inspector is a Rust library from Firecrawl that classifies PDFs as text-based, scanned, image-based, or mixed, extracts native text into Markdown, and identifies pages that may need OCR.
The project provides Rust, Node.js, Python, command-line, and browser WebAssembly paths for keeping native-text PDF inspection local before a workflow decides whether to use OCR. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A local PDF classifier and extractor
It inspects PDF content, separates native-text documents from scanned or image-based material, and can turn supported text, headings, lists, links, tables, and layout cues into Markdown.
Why it stands out
OCR routing can happen page by page
The result includes document type, confidence, and pages that appear to need OCR, so a pipeline can use direct extraction where it works and reserve OCR for the pages that need another route.
Availability
Several local integration paths
The public repository includes Node.js and browser packages, a Rust crate and CLI, Python bindings, API notes, examples, tests, and a local browser demo.
Why it matters
What makes it useful
A PDF pipeline does not need the same expensive path for every document. pdf-inspector gives builders a local first step: extract usable native text immediately, identify mixed or scanned pages, and send only the harder material to OCR when the workflow calls for it.
What to know
Where it fits
Use it before OCR, search, RAG, dataset preparation, or agent ingestion. It is a developer library rather than a finished document app, and its direct extraction path complements OCR tools instead of replacing them for scanned pages.
Notable points
What stands out
The repository documents classification strategies, per-page OCR hints, layout and Markdown handling, package options, and a reproducible benchmark branch. Its published benchmark results are project-run measurements on a named corpus and machine, so readers can inspect the setup and test their own documents.
Before using
What to review
Direct extraction does not perform OCR; scanned, image-based, mixed, or poorly encoded pages may still need a separate OCR route.
Test the PDFs that matter for columns, tables, headings, forms, unusual fonts, reading order, and mixed page types before relying on the Markdown downstream.
Keep the parser and its dependencies current, and apply normal resource limits when processing untrusted or unusually large PDFs.
Check the current package and platform support for the chosen Rust, Node.js, Python, command-line, or browser path.
Reader fit
Who may find it relevant
Builders routing PDFs between direct text extraction and OCR.
Teams preparing documents for search, RAG, datasets, or agent context without sending every native-text PDF to an external service.
Developers who want one parser exposed through server, command-line, Python, and browser integrations.
Less relevant for readers looking for a finished document chat app or a tool that performs OCR itself.
Editorial note
Why LifeHubber lists it
LifeHubber lists pdf-inspector because it makes an early document decision practical: which pages already contain usable text, and which need OCR. That split can keep simple PDFs on a fast local path while giving harder pages a deliberate fallback.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare the next step in the document pipeline.
Classification is only the first decision. These projects show a lighter general parser and a model-backed document conversion route.
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