PulseBench-Tab: New Multilingual Benchmark for Evaluating Table Extraction from Documents
Researchers introduced PulseBench-Tab, an open multilingual benchmark containing 1,820 human-annotated tables across 9 languages for evaluating table extraction from document images. The benchmark includes tables from real-world financial, government, and regulatory documents, with a novel evaluation metric called T-LAG that assesses both structural and content accuracy. This resource addresses a gap in standardized evaluation for table extraction systems, which are critical for automated document processing across multiple languages and scripts.
PulseBench-Tab is a new open-source benchmark designed to evaluate table extraction systems on document images in a multilingual context. The dataset comprises 1,820 human-annotated tables spanning 9 languages and 4 different scripts (Latin, CJK, Arabic, and Cyrillic), sourced from 380 real-world documents including financial filings, government reports, and regulatory disclosures. Tables in the benchmark vary significantly in complexity, ranging from 2 to 1,183 cells, with nearly half containing merged or spanning cells that increase extraction difficulty. The researchers also introduced T-LAG (Table Logical Adjacency Graph), a novel evaluation metric that models tables as directed graphs and measures both structural and content fidelity through optimal bipartite matching. The team evaluated 9 commercial and open-source table extraction systems and provided per-language performance breakdowns, with all dataset materials, scoring code, and system outputs made publicly available.
What's missing
The study does not discuss potential limitations of the human annotation process, inter-annotator agreement rates, or how annotation disagreements were resolved. Additionally, the paper does not specify which commercial systems were evaluated or provide details on the computational requirements for the T-LAG evaluation metric.
What different sources said
- arXiv cs.CLCenter
PulseBench-Tab: A Multilingual Benchmark for Table Extraction with Graph-Based Evaluation
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