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

arXiv: https://arxiv.org/abs/2510.02133 · HTML: https://mlsanity.com/paper/2510.02133/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 61 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 30 of 32)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://mlsanity.com/paper/2108.04539/) (2021)
- [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://mlsanity.com/paper/2104.08836/) (2021)
- [Chargrid: Towards Understanding 2D Documents](https://mlsanity.com/paper/1809.08799/) (2018)
- [Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network](https://mlsanity.com/paper/1706.02337/) (2017)
- [StrucTexT: Structured Text Understanding with Multi-Modal Transformers](https://mlsanity.com/paper/2108.02923/) (2021)
- [Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction](https://mlsanity.com/paper/2303.04132/) (2023)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [DocILE Benchmark for Document Information Localization and Extraction](https://mlsanity.com/paper/2302.05658/) (2023)
- [GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation](https://mlsanity.com/paper/2504.02782/) (2025)
- [GeoLayoutLM: Geometric Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2304.10759/) (2023)
- [An Empirical Study of GPT-4o Image Generation Capabilities](https://mlsanity.com/paper/2504.05979/) (2025)
- [Entity Relation Extraction as Dependency Parsing in Visually Rich Documents](https://mlsanity.com/paper/2110.09915/) (2021)
- [Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy](https://mlsanity.com/paper/2412.17759/) (2024)
- [Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems](https://mlsanity.com/paper/2505.18366/) (2025)
- [LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents](https://mlsanity.com/paper/2411.14962/) (2024)
- [Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts](https://mlsanity.com/paper/2412.03590/) (2024)
- [Tokenization Matters: Improving Zero-Shot NER for Indic Languages](https://mlsanity.com/paper/2504.16977/) (2025)
- [Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents](https://mlsanity.com/paper/2108.02899/) (2021)
- [Aligning LLMs for Multilingual Consistency in Enterprise Applications](https://mlsanity.com/paper/2509.23659/) (2025)
- [FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding](https://mlsanity.com/paper/2505.17330/) (2025)
- [Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization](https://mlsanity.com/paper/2502.13108/) (2025)
- [Synthetic Document Generator for Annotation-free Layout Recognition](https://mlsanity.com/paper/2111.06016/) (2021)
- [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://mlsanity.com/paper/2509.14270/) (2025)

## Built on by (most cited first, 2 of 2)

- [Do Image-Text Metrics Respect Semantic Invariances?](https://mlsanity.com/paper/2605.24702/) (2026)
- [Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis](https://mlsanity.com/paper/2607.04636/) (2026)

## Abstract (arXiv)

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.