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ARXIV:2603.18652 · DOCUMENT AI · SUBMITTED 20 MAR · 21:29 UTC · FRESHNESS STALE
ARXIV:2603.18652DOCUMENT AISUBMITTED 20 MAR · 21:29 UTCFRESHNESS STALEPius Horn · Janis Keuper · arXiv
A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection.
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Pain A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection.
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A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground…
Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a human validation study comprising over 1,500 quality judgments on extracted table pairs, we show that LLM-based evaluation achieves substantially higher correlation with…
Document AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection.
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10.48550/arXiv.2603.18652A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection.
Abstract
Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realistic complexity and diversity. As our central methodological contribution, we apply LLM-as-a-judge for semantic table evaluation, integrated into a matching pipeline that accommodates inconsistencies in parser outputs. Through a human validation study comprising over 1,500 quality judgments on extracted table pairs, we show that LLM-based evaluation achieves substantially higher correlation with human judgment (Pearson r=0.93) compared to Tree Edit Distance-based Similarity (TEDS, r=0.68) and Grid Table Similarity (GriTS, r=0.70). Evaluating 21 contemporary PDF parsers across 100 synthetic documents containing 451 tables reveals significant performance disparities. Our results offer practical guidance for selecting parsers for tabular data extraction and establish a reproducible, scalable evaluation methodology for this critical task. Code and data: https://github.com/phorn1/pdf-parse-bench Metric study and human evaluation: https://github.com/phorn1/table-metric-study
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PROBLEM
A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tabl...
METHOD
Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a human validation study comprising over 1,500 quality judgments on extracted table pairs, we show that LLM-based evaluation achieves substantially higher correlation with human judgment (Pearson...
WHY NOW
Document AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realistic complexity and diversity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realistic complexity and diversity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through a human validation study comprising over 1,500 quality judgments on extracted table pairs, we show that LLM-based evaluation achieves substantially higher correlation with human judgment (Pearson r=0.93) compared to Tree Edit Distance-based Similarity (TEDS, r=0.68) and Grid Table Similarity (GriTS, r=0.70). A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Document AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new LLM-based evaluation framework for PDF table extraction that significantly outperforms existing metrics, providing practical guidance for parser selection.
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