FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models

• URL: http://arxiv.org/abs/2510.02133v1
• Date: Thu, 02 Oct 2025 15:42:35 GMT
• Title: FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models
• Authors: Karan Dua, Hitesh Laxmichand Patel, Puneet Mittal, Ranjeet Gupta, Amit Agarwal, Praneet Pabolu, Srikant Panda, Hansa Meghwani, Graham Horwood, Fahad Shah,
• Abstract summary: Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets.<n>We introduce FlexDoc, a scalable synthetic data generation framework.<n>We show that FlexDoc improves the absolute F1 Score by up to 11% when used to augment real datasets.
• Score: 4.013756026582041
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such data is prohibitively expensive due to privacy constraints, legal restrictions, and the sheer volume of manual annotation needed - costs that can scale into millions of dollars. We introduce FlexDoc, a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. By probabilistically modeling layout patterns, visual structure, and content variability, FlexDoc enables the controlled generation of diverse document variants at scale. Experiments on Key Information Extraction (KIE) tasks demonstrate that FlexDoc-generated data improves the absolute F1 Score by up to 11% when used to augment real datasets, while reducing annotation effort by over 90% compared to traditional hard-template methods. The solution is in active deployment, where it has accelerated the development of enterprise-grade document understanding models while significantly reducing data acquisition and annotation costs.

Related papers

• 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)
• UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters [55.34921520578968]
  Vision-language models (VLMs) have achieved impressive unified recognition of text and formulas.<n>We propose UniRec-0.1B, a unified recognition model with only 0.1B parameters.<n>It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents.
  arXiv  Detail & Related papers  (2025-12-24T10:35:21Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• SynDoc: A Hybrid Discriminative-Generative Framework for Enhancing Synthetic Domain-Adaptive Document Key Information Extraction [29.174133313633817]
  Domain-specific Visually Rich Document Understanding (VRDU) presents significant challenges due to the complexity and sensitivity of documents in fields such as medicine, finance, and material science.<n>Existing Large (Multimodal) Language Models (LLMs/MLLMs) achieve promising results but face limitations such as hallucinations, inadequate domain adaptation, and reliance on extensive fine-tuning datasets.<n>This paper introduces SynDoc, a novel framework that combines discriminative and generative models to address these challenges.
  arXiv  Detail & Related papers  (2025-09-27T12:01:52Z)
• Docopilot: Improving Multimodal Models for Document-Level Understanding [87.60020625241178]
  We present a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents.<n>This dataset includes diverse document structures, extensive cross-page dependencies, and real question-answer pairs derived from the original documents.<n>Building on the dataset, we develop a native multimodal model, Docopilot, which can accurately handle document-level dependencies without relying on RAG.
  arXiv  Detail & Related papers  (2025-07-19T16:03:34Z)
• DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral [11.336757553731639]
  Acquiring structured data from domain-specific, image-based documents is crucial for many downstream tasks.<n>Many documents exist as images rather than as machine-readable text, which requires human annotation to train automated extraction systems.<n>We present DocSpiral, the first Human-in-the-Spiral assistive document annotation platform.
  arXiv  Detail & Related papers  (2025-05-06T06:02:42Z)
• Relation-Rich Visual Document Generator for Visual Information Extraction [12.4941229258054]
  We propose a Relation-rIch visual Document GEnerator (RIDGE) that addresses these limitations through a two-stage approach.<n>Our method significantly enhances the performance of document understanding models on various VIE benchmarks.
  arXiv  Detail & Related papers  (2025-04-14T19:19:26Z)
• OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations [22.336858733121158]
  We introduce OmniDocBench, a novel benchmark featuring high-quality annotations across nine document sources.<n>We conduct a thorough evaluation of both pipeline-based methods and end-to-end vision-language models.
  arXiv  Detail & Related papers  (2024-12-10T16:05:56Z)
• BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks [57.589795399265945]
  We introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks.<n>We also introduce BigDocs-Bench, a benchmark suite with 10 novel tasks.<n>Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o.
  arXiv  Detail & Related papers  (2024-12-05T21:41:20Z)
• DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception [16.301481927603554]
  We introduce Doc-YOLO, a novel approach that enhances accuracy while maintaining speed advantages. For robust document pre-training, we introduce the Mesh-candidate BestFit algorithm. In terms of model optimization, we propose a Global-to-Local Controllable Receptive Module.
  arXiv  Detail & Related papers  (2024-10-16T14:50:47Z)
• DocMamba: Efficient Document Pre-training with State Space Model [56.84200017560988]
  We present DocMamba, a novel framework based on the state space model.<n>It is designed to reduce computational complexity to linear while preserving global modeling capabilities.<n>Experiments on the HRDoc confirm DocMamba's potential for length extrapolation.
  arXiv  Detail & Related papers  (2024-09-18T11:34:28Z)
• mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding [55.4806974284156]
  Document understanding refers to automatically extract, analyze and comprehend information from digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs) have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition.
  arXiv  Detail & Related papers  (2023-07-04T11:28:07Z)
• XDoc: Unified Pre-training for Cross-Format Document Understanding [84.63416346227176]
  XDoc is a unified pre-trained model which deals with different document formats in a single model. XDoc achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models.
  arXiv  Detail & Related papers  (2022-10-06T12:07:18Z)
• Leveraging Graph to Improve Abstractive Multi-Document Summarization [50.62418656177642]
  We develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries.
  arXiv  Detail & Related papers  (2020-05-20T13:39:47Z)

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.