FormStruct-Bench:A Hierarchical and Diagnostic Benchmark for Table-Form Document Structure Recognition
- URL: http://arxiv.org/abs/2608.10396v1
- Date: Tue, 11 Aug 2026 02:44:34 GMT
- Title: FormStruct-Bench:A Hierarchical and Diagnostic Benchmark for Table-Form Document Structure Recognition
- Abstract summary: We introduce FormStruct-Bench, a hierarchical and diagnostic benchmark that evaluates table-form document structure recognition.<n>To construct auditable ground truth at scale, we annotate 70 reusable templates and expand them into 7,000 verified instances.<n>Across 14 API-hosted and locally deployable systems plus two SFT variants, the best document-level score reaches 83.85%, whereas the best reported fine-grained structural score remains below 18%.
- Score: 3.879207672674058
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Transforming table-form documents into machine-processable records requires recovering not only their visible content but also the multilevel structure that organizes it. However, existing benchmarks evaluate either holistic document outputs or conventional table grids, and their aggregate scores provide little insight into where structural failures occur. We introduce FormStruct-Bench, a hierarchical and diagnostic benchmark that evaluates table-form document structure recognition at both the document level and progressively finer component levels, allowing aggregate performance to be traced back to specific structural failure modes. To construct auditable ground truth at scale, we annotate 70 reusable templates and expand them into 7,000 verified instances through a provenance-preserving Director--Artist--Verifier pipeline; all 1,100 instances in the template-disjoint test set additionally receive human review. Our evaluation protocol uses five primary metrics and three structure-specific diagnostics across page, schema, and component levels, together with slices over difficulty, structural constraints, and visual degradation. Across 14 API-hosted and locally deployable systems plus two SFT variants, the best document-level score reaches 83.85%, whereas the best reported fine-grained structural score remains below 18%. These results reveal a pronounced gap between reading document content and recovering the hierarchy and regional organization required for reliable table-form understanding.
Related papers
- LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents [7.641999919052611]
We introduce textscLongDocBench, comprising 85 real-world financial reports, textbooks, and academic papers spanning 2,582 pages, with up to 105 pages per document.<n>It provides human-verified annotations for 3,937 heading nodes (mean node depth 3.55; maximum depth 9) and 3,258 contextual relationships annotated across 2,680 table and figure objects.<n>Long-document question-answering experiments show that human-verified hierarchies and contextual relationships improve reasoning, with their combination providing complementary benefits.
arXiv Detail & Related papers (2026-08-15T06:25:26Z) - Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction [4.062546658033461]
We introduce Doc2DB-Bench, a benchmark for Document-to-Database construction.<n>It contains 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells.
arXiv Detail & Related papers (2026-08-09T03:58:39Z) - Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing [53.41293908252118]
We introduce Dr. DocBench, a difficulty-aware benchmark for expert-level document parsing.<n>Dr. DocBench spans 52 BISAC domains and selects documents through failure-based sampling.<n>It contains 4,514 annotated pages from long documents averaging around 100 pages, with 65k high-quality annotations for layout, reading order, hierarchical relations, and domain-specific visual contents.<n>Our analysis reveals substantial failures across subjects, content types, and structural attributes, highlighting Dr. DocBench as a comprehensive testbed for diagnosing and advancing document intelligence.
arXiv Detail & Related papers (2026-05-31T18:35:30Z) - HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering [19.052998569565627]
HiKEY is a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal.<n> Experiments on ODQA benchmarks demonstrate that HiKEY significantly outperforms page- and chunk-based baselines.
arXiv Detail & Related papers (2026-05-28T08:42:21Z) - MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing [74.84107522458798]
MPDocBench-Parse is a benchmark for multi-page document parsing in real-world applications.<n>It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles.
arXiv Detail & Related papers (2026-05-21T07:36:41Z) - MoDora: Tree-Based Semi-Structured Document Analysis System [62.01015188258797]
Semi-structured documents integrate diverse interleaved data elements arranged in various and often irregular layouts.<n>MoDora is an LLM-powered system for semi-structured document analysis.<n> Experiments show MoDora outperforms baselines by 5.97%-61.07% in accuracy.
arXiv Detail & Related papers (2026-02-26T14:48:49Z) - SPIRE: Structure-Preserving Interpretable Retrieval of Evidence [0.09558392439655013]
We present a structure-aware retrieval pipeline that operates over tree-structured documents.<n>We define a small set of document primitives--paths and path sets.<n>Global contextualization adds the non-local scaffolding needed to make a selection intelligible.<n>Local contextualization expands a seed selection within its structural neighborhood to obtain a compact, context-rich view.
arXiv Detail & Related papers (2026-02-12T03:46:10Z) - MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns [80.05126590825121]
MonkeyOCR v1.5 is a unified vision-language framework that enhances both layout understanding and content recognition.<n>To address complex table structures, we propose a visual consistency-based reinforcement learning scheme.<n>Two specialized modules, Image-Decoupled Table Parsing and Type-Guided Table Merging, are introduced to enable reliable parsing of tables.
arXiv Detail & Related papers (2025-11-13T15:12:17Z) - UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis [7.057192434574117]
We propose a unified relation prediction approach for HDSA, called UniHDSA.<n>UniHDSA treats various HDSA sub-tasks as relation prediction problems and consolidates relation prediction labels into a unified label space.<n>To validate the effectiveness of UniHDSA, we develop a multimodal end-to-end system based on Transformer architectures.
arXiv Detail & Related papers (2025-03-20T06:44:47Z) - SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding [55.48936731641802]
We present the SRFUND, a hierarchically structured multi-task form understanding benchmark.
SRFUND provides refined annotations on top of the original FUNSD and XFUND datasets.
The dataset includes eight languages including English, Chinese, Japanese, German, French, Spanish, Italian, and Portuguese.
arXiv Detail & Related papers (2024-06-13T02:35:55Z) - Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis [9.340346869932434]
We propose a tree construction based approach that addresses multiple subtasks concurrently.
We present an effective end-to-end solution based on this framework to demonstrate its performance.
Our end-to-end system achieves state-of-the-art performance on two large-scale document layout analysis datasets.
arXiv Detail & Related papers (2024-01-22T12:00:37Z) - Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text
Documents via Semantic-Oriented Hierarchical Graphs [79.0426838808629]
We propose TAT-DQA, i.e. to answer the question over a visually-rich table-text document.
Specifically, we propose a novel Doc2SoarGraph framework with enhanced discrete reasoning capability.
We conduct extensive experiments on TAT-DQA dataset, and the results show that our proposed framework outperforms the best baseline model by 17.73% and 16.91% in terms of Exact Match (EM) and F1 score respectively on the test set.
arXiv Detail & Related papers (2023-05-03T07:30:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.