PDF Oxide vs PyMuPDF: which is better for commercial projects?

PDF Oxide is MIT licensed and 5x faster, making it superior for commercial use without AGPL restrictions, but PyMuPDF has more mature features for advanced editing. Check if PDF Oxide's extraction-focused toolkit meets all your needs before switching.

How to extract tables from a PDF using PDF Oxide in Python?

Use the `extract_tables` method on a PdfDocument object: `tables = doc.extract_tables(page_number)` returns structured table data. This is ideal for data pipelines, though complex table layouts might require additional processing.

Does PDF Oxide support PDF/A compliant documents?

Yes, it has a 100% pass rate on the veraPDF corpus, which includes PDF/A compliance tests, ensuring reliable extraction from PDF/A files. However, it doesn't validate or create PDF/A documents specifically.

Can I use PDF Oxide in a browser with JavaScript?

Yes, via its WebAssembly (WASM) support, which allows PDF processing in Node.js or browsers, though performance may be slower than native builds. Install the `pdf-oxide-wasm` npm package for integration.

What's the performance impact for very large PDF files?

Benchmarks show a mean of 0.8ms per document, but performance varies with file size and complexity; the CLI and APIs support incremental processing and page ranges to handle large files efficiently.

How do I install PDF Oxide on Windows without Homebrew?

For Python, use `pip install pdf_oxide` with pre-built wheels; for Rust, add it to Cargo.toml; the CLI can be installed via `cargo install pdf_oxide_cli` or using cargo-binstall for pre-built binaries.

Open-Awesome

pdf_oxide

Apache-2.0Rustv0.3.41

A high-performance PDF toolkit for text/image extraction, markdown conversion, and PDF editing, built in Rust with Python, WASM, CLI, and MCP server bindings.

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717 stars78 forks0 contributors

What is pdf_oxide?

PDF Oxide is a high-performance, open-source PDF processing library built with a Rust core. It provides fast and reliable tools for extracting text and images from PDFs, converting them to markdown or HTML, and creating or editing PDF documents. It solves the problem of slow and restrictive PDF processing in data pipelines, AI applications, and document automation.

Target Audience

Developers and data engineers working with document processing pipelines, AI/ML practitioners building RAG systems, and anyone needing fast, reliable PDF text/image extraction or conversion in Python, Rust, or JavaScript environments.

Value Proposition

Developers choose PDF Oxide for its exceptional speed (5-15x faster than popular alternatives), 100% reliability on a large test suite, permissive MIT/Apache-2.0 license (no AGPL restrictions), and its unified multi-platform API covering Rust, Python, WASM, CLI, and AI assistants via MCP.

Overview

The fastest PDF library for Python and Rust. Text extraction, image extraction, markdown conversion, PDF creation & editing. 0.8ms mean, 5× faster than industry leaders, 100% pass rate on 3,830 PDFs. MIT/Apache-2.0.

Use Cases

Best For

Building high-throughput document processing pipelines for RAG and LLM applications
Extracting structured text and table data from thousands of PDFs at scale
Converting academic papers or reports into clean markdown for analysis
Creating a local, privacy-focused PDF tool for AI assistants via the MCP server
Replacing AGPL-licensed PDF libraries like PyMuPDF in commercial projects
Programmatically generating, merging, or filling PDF forms and documents

Not Ideal For

Projects requiring optical character recognition (OCR) for scanned PDFs, as it only extracts text from native layers.
Environments with strict no-native-extension policies that mandate pure Python or JavaScript solutions.
Applications needing advanced PDF features like 3D content, multimedia annotations, or complex digital signature validation.
Legacy systems deeply integrated with older PDF libraries where migration effort outweighs performance gains.

Pros & Cons

Pros

Blazing Fast Performance

Benchmarked at 0.8ms mean per document on 3,830 PDFs, making it 5x faster than PyMuPDF and 15x faster than pypdf for text extraction.

Unmatched Reliability

Achieves a 100% pass rate on the test corpus with zero panics or timeouts, ensuring consistent extraction across diverse real-world PDFs.

Multi-Platform Versatility

Provides native APIs for Rust and Python, WASM for JavaScript, a CLI tool, and an MCP server for AI assistants, covering a wide range of development environments.

Permissive Licensing

Dual-licensed under MIT/Apache-2.0, allowing unrestricted commercial use without the copyleft restrictions of AGPL alternatives like PyMuPDF.

Cons

No OCR Capability

Lacks optical character recognition support, so it cannot extract text from scanned or image-based PDFs, limiting its applicability for legacy documents.

Rust Dependency Overhead

Requires a Rust toolchain for building from source, which can complicate deployment in environments where lightweight, interpreted language solutions are preferred.

Limited Maturity

As a version 0.3 project, it has a smaller ecosystem and may introduce breaking changes, with fewer third-party integrations compared to established libraries.

Open Source Alternative To

pdf_oxide is an open-source alternative to the following products:

pdfminer

PDFMiner is a Python library for extracting text and metadata from PDF documents, focusing on detailed layout analysis.

pdfplumber

pdfplumber is a Python library for extracting and analyzing text, tables, and visual data from PDF files with high precision.

PyMuPDF

PyMuPDF is a Python binding for the MuPDF library, providing tools to manipulate PDF files including rendering, text extraction, and annotation.

pypdf

pypdf is a pure-Python library for reading, splitting, merging, cropping, and transforming PDF files without external dependencies.

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