Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents

• URL: http://arxiv.org/abs/2606.06242v1
• Date: Thu, 04 Jun 2026 14:47:40 GMT
• Title: Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents
• Abstract summary: We introduce a benchmark dataset and evaluation framework for textitdata snapshot extraction.<n>We benchmarked multiple open-source layout detection models and evaluated both detection performance and spatial extraction quality.<n>These findings highlight a persistent gap between generic document layout analysis and operationally useful data snapshot extraction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for extracting visual content from documents are largely built around generic document layout analysis, where figures and tables are treated as uniformly relevant document objects rather than semantically meaningful analytical artifacts. In this work, we introduce a benchmark dataset and evaluation framework for \textit{data snapshot extraction}, the task of identifying and localizing semantically meaningful visual artifacts within institutional documents. The benchmark spans humanitarian reports, World Bank policy research working papers, and project appraisal documents, and includes annotations for figures and tables that contain reusable analytical information. Using this dataset, we benchmarked multiple open-source layout detection models and evaluated both detection performance and spatial extraction quality. Our results show that current models struggle to generalize to operational institutional documents despite strong performance on conventional academic benchmarks. Common failure modes include confusion between analytical and non-analytical content, fragmentation of composite analytical artifacts, and incomplete extraction of contextual information required for interpretation. These findings highlight a persistent gap between generic document layout analysis and operationally useful data snapshot extraction. We release the source PDFs, annotation dataset, metadata, and source code to support future research in operational document intelligence. The dataset is available at https://huggingface.co/datasets/ai4data/data-snapshot and the source code is available at https://github.com/worldbank/ai4data/tree/main/experimental/data-snapshot.

Related papers

• Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement [0.0]
  We present a framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents.<n>A lightweight model trained on general research literature generates candidate dataset mentions from unlabeled domain documents.<n>We evaluate the resulting model on an independent gold-standard benchmark of 1,706 text passages spanning research, humanitarian, and operational documents.
  arXiv  Detail & Related papers  (2026-09-10T18:34:08Z)
• Bounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets [49.345169562968415]
  Bounding Box Label Propagation (BBLP) is a pseudo-labelling framework for object detection.<n>It produces high-quality class annotations of bounding boxes.<n>It achieves a mAP of 54.0%, corresponding to 81.6% of fully supervised performance, while using only 10% labelled data.
  arXiv  Detail & Related papers  (2026-06-16T08:04:27Z)
• DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion [5.342168661302001]
  We propose a novel framework for controllable synthetic document generation using Vision-Language Models (VLMs)<n>Our approach generates visually plausible and semantically consistent synthetic documents that follow the distribution of an existing source dataset.<n>We show that our framework achieves on average $87%$ of the performance of the full real-world dataset.
  arXiv  Detail & Related papers  (2026-02-25T11:52:13Z)
• ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images [19.490609860018804]
  We introduce ExStrucTiny, a new benchmark dataset for structured Information Extraction (IE) from document images.<n>Built through a novel pipeline combining manual and synthetic human-validated samples, ExStrucTiny covers more varied document types and extraction scenarios.<n>We analyze open and closed Vision Language Models on this benchmark, highlighting challenges such as adaptation, query under-specification, and schema adaptation.
  arXiv  Detail & Related papers  (2026-02-12T17:38:57Z)
• LongDA: Benchmarking LLM Agents for Long-Document Data Analysis [55.32211515932351]
  LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck.<n>LongTA is a tool-augmented agent framework that enables document access, retrieval, and code execution.<n>Our experiments reveal substantial performance gaps even among state-of-the-art models.
  arXiv  Detail & Related papers  (2026-01-05T23:23:16Z)
• DREAM: Document Reconstruction via End-to-end Autoregressive Model [53.51754520966657]
  We present an innovative autoregressive model specifically designed for document reconstruction, referred to as Document Reconstruction via End-to-end Autoregressive Model (DREAM)<n>We establish a standardized definition of the document reconstruction task, and introduce a novel Document Similarity Metric (DSM) and DocRec1K dataset for assessing the performance of the task.
  arXiv  Detail & Related papers  (2025-07-08T09:24:07Z)
• Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents [8.49076413640561]
  The model is pre-trained and subsequently fine-tuned for various document image analysis tasks. The proposed model achieved impressive results across all tasks, with an accuracy of 95.87% on the RVL-CDIP dataset for document classification.
  arXiv  Detail & Related papers  (2023-10-25T10:22:30Z)
• Leveraging Contextual Information for Effective Entity Salience Detection [21.30389576465761]
  We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. We also show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.
  arXiv  Detail & Related papers  (2023-09-14T19:04:40Z)
• Modeling Entities as Semantic Points for Visual Information Extraction in the Wild [55.91783742370978]
  We propose an alternative approach to precisely and robustly extract key information from document images. We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities. The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-23T08:21:16Z)
• DocBank: A Benchmark Dataset for Document Layout Analysis [114.81155155508083]
  We present textbfDocBank, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents.
  arXiv  Detail & Related papers  (2020-06-01T16:04:30Z)
• SciREX: A Challenge Dataset for Document-Level Information Extraction [56.83748634747753]
  It is challenging to create a large-scale information extraction dataset at the document level. We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
  arXiv  Detail & Related papers  (2020-05-01T17:30:10Z)

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.