Reproduce “A Comparative Study of PDF Parsing Tools Across Diverse Document Categories” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Narayan S. Adhikari, Shradha Agarwal
A comparative study evaluated 10 actively maintained open-source PDF parsing tools for full-text extraction and table detection across six diverse document categories using the DocLayNet dataset. The research found that rule-based tools perform effectively for general text in most document types, while learning-based models like Nougat and Table Transformer achieve superior performance for scientific documents and complex table detection respectively.
Key insights
- Rule-based PDF parsing tools generally achieve high accuracy for full-text extraction in straightforward document types like Financial, Law, and Manuals.
- Complex document categories, such as Scientific articles with mathematical expressions and patents, significantly challenge rule-based parsers, leading to notable performance drops.
- Learning-based models like Nougat for text and Table Transformer (TATR) for tables demonstrate superior performance in handling complex layouts and diverse table structures, outperforming rule-based methods in these areas.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.