Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

Amit Agarwal, Priyaranjan Pattnayak, Bhargava Kumar, Hitesh Laxmichand Patel, Tejaswini Kumar, Srikant Panda

Abstract:

The development of robust Document AI models has
been constrained by limited access to high-quality, labeled
datasets, primarily due to data privacy concerns, scarcity, and
the high cost of manual annotation. Traditional methods of
synthetic data generation, such as text and image augmentation,
have proven effective for increasing data diversity but often fail
to capture the complex layout structures present in real world
documents. This paper proposes a novel approach to synthetic
document layout generation using Graph Neural Networks
(GNNs). By representing document elements (e.g., text blocks,
images, tables) as nodes in a graph and their spatial relationships
as edges, GNNs are trained to generate realistic and diverse
document layouts. This method leverages graph-based learning
to ensure structural coherence and semantic consistency,
addressing the limitations of traditional augmentation
techniques. The proposed framework is evaluated on tasks such
as document classification, named entity recognition (NER), and
information extraction, demonstrating significant performance
improvements. Furthermore, we address the computational
challenges of GNN based synthetic data generation and propose
solutions to mitigate domain adaptation issues between synthetic
and real-world datasets. Our experimental results show that
graph-augmented document layouts outperform existing
augmentation techniques, offering a scalable and flexible solution
for training Document AI models.