# A Comparative Study of PDF Parsing Tools Across Diverse Document Categories

arXiv: https://arxiv.org/abs/2410.09871 · HTML: https://mlsanity.com/paper/2410.09871/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [Benchmarking Document Parsers on Mathematical Formula Extraction from PDFs](https://mlsanity.com/paper/2512.09874/) (2025)
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- [AQUA: A Large Language Model for Aquaculture & Fisheries](https://mlsanity.com/paper/2507.20520/) (2025)
- [Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG](https://mlsanity.com/paper/2604.12047/) (2026)
- [Benchmarking Large Language Models on Reference Extraction and Parsing in the Social Sciences and Humanities](https://mlsanity.com/paper/2603.13651/) (2026)
- [Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation](https://mlsanity.com/paper/2604.00003/) (2026)
- [GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering](https://mlsanity.com/paper/2608.22634/) (2026)

## Abstract (arXiv)

PDF is one of the most prominent data formats, making PDF parsing crucial for information extraction and retrieval, particularly with the rise of RAG systems. While various PDF parsing tools exist, their effectiveness across different document types remains understudied, especially beyond academic papers. Our research aims to address this gap by comparing 10 popular PDF parsing tools across 6 document categories using the DocLayNet dataset. These tools include PyPDF, pdfminer-six, PyMuPDF, pdfplumber, pypdfium2, Unstructured, Tabula, Camelot, as well as the deep learning-based tools Nougat and Table Transformer(TATR). We evaluated both text extraction and table detection capabilities. For text extraction, PyMuPDF and pypdfium generally outperformed others, but all parsers struggled with Scientific and Patent documents. For these challenging categories, learning-based tools like Nougat demonstrated superior performance. In table detection, TATR excelled in the Financial, Patent, Law & Regulations, and Scientific categories. Table detection tool Camelot performed best for tender documents, while PyMuPDF performed superior in the Manual category. Our findings highlight the importance of selecting appropriate parsing tools based on document type and specific tasks, providing valuable insights for researchers and practitioners working with diverse document sources.