Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence

TL;DR

This paper provides a comprehensive overview of recent data generation methods for Document Intelligence, proposing a unified framework and evaluation approach to address challenges and advance the field.

Contribution

It introduces a novel taxonomy and evaluation framework for data generation in DI, unifying diverse methodologies and highlighting key challenges and future directions.

Findings

Organized data generation methods into four resource-centric paradigms.

Established a multi-level evaluation framework for assessing data quality and utility.

Identified critical challenges like fidelity gaps and co-evolutionary ecosystems.

Abstract

The advancement of Document Intelligence (DI) demands large-scale, high-quality training data, yet manual annotation remains a critical bottleneck. While data generation methods are evolving rapidly, existing surveys are constrained by fragmented focuses on single modalities or specific tasks, lacking a unified perspective aligned with real-world workflows. To fill this gap, this survey establishes the first comprehensive technical map for data generation in DI. Data generation is redefined as supervisory signal production, and a novel taxonomy is introduced based on the "availability of data and labels." This framework organizes methodologies into four resource-centric paradigms: Data Augmentation, Data Generation from Scratch, Automated Data Annotation, and Self-Supervised Signal Construction. Furthermore, a multi-level evaluation framework is established to integrate intrinsic quality and extrinsic utility, compiling performance gains across diverse DI benchmarks. Guided by this unified structure, the methodological landscape is dissected to reveal critical challenges such as fidelity gaps and frontiers including co-evolutionary ecosystems. Ultimately, by systematizing this fragmented field, data generation is positioned as the central engine for next-generation DI.