AI Briefingpdf-inspector: PDF text extraction within 200ms by skipping OCR for speed
firecrawl/pdf-inspector
About the project
Reduce text-based PDF processing time to within 200ms. Since approximately 54% of all PDFs are text-based documents that do not require OCR, they are automatically classified to skip heavy OCR services. Written in Rust, it delivers consistent performance across various environments, including Python, Node.js, and browser WebAssembly.
Beyond simple text extraction, it generates structured Markdown based on positional awareness. By analyzing font size and layout, it automatically recognizes H1–H4 headings, lists, code blocks, and tables, while also supporting newspaper-style multi-column layouts and RTL text. Table detection operates in dual mode, utilizing both straight-line drawings and text alignment within the PDF.
Processing 200 documents takes 0.47 seconds according to opendataloader-bench, demonstrating overwhelming speed compared to existing tools. Compared to LiteParse, PyMuPDF4LLM, and others, it achieved high scores in overall performance, reading order accuracy, and table recognition rate. It is suitable for quickly processing structured text PDFs locally, such as reports, research papers, and financial documents.
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.
Rust
This introduction was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report errors, attribution issues, or removal requests via Contact.