firecrawl/pdf-inspector
Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 36 days ago, with 12 contributors on GitHub.
Peaked at #5 in the weekly ranking the week of Aug 30, its best in the past year.
Use Cases & Benefits
- Provides a fast Rust library for PDF classification, text extraction, and Markdown conversion with position and layout awareness.
- Enables smart routing by quickly detecting scanned versus text-based PDFs, reducing reliance on slow OCR services.
- Use for building efficient document processing pipelines that route PDFs to OCR only when necessary, saving time and cost.
- Use for extracting structured text, tables, and formatting from PDFs into clean Markdown for content repurposing or analysis.
- Use for integrating PDF inspection capabilities into Python, Node.js, or Rust applications requiring fast, local PDF parsing.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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.
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.
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.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+7,799 stars over 90 days across 21 snapshot days; best day +636 on Aug 7.
Forks
1.3K+530
Open issues
255+174
Compared with peers in Documentation
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| hexojs/hexo | 41.8K | — | 55 | 186 days ago |
| ScrapeGraphAI/Scrapegraph-ai | 32.4K | — | 64 | 26 days ago |
| squidfunk/mkdocs-material | 27.6K | — | 65 | 8 days ago |
| firecrawl/pdf-inspector | 19.8K | — | 30 | 36 days ago |
| MrLesk/Backlog.md | 7.3K | — | 59 | 17 days ago |
| geekjourneyx/md2wechat-skill | 3.8K | — | 26 | 45 days ago |
| Category median | 27.6K | — | 59 | — |
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