Deep Learning based Visually Rich Document Content Understanding: A Survey

TL;DR

This survey reviews deep learning methods for understanding visually rich documents, emphasizing multimodal models that combine text, layout, and visual cues to improve information extraction across various domains.

Contribution

It provides a comprehensive categorization, comparison, and analysis of deep learning frameworks for VRD content understanding, highlighting strengths, limitations, and future directions.

Findings

Deep learning models significantly enhance VRD content understanding.

Multimodal fusion techniques are crucial for effective information extraction.

Pretraining strategies improve model performance and generalization.

Abstract

Visually Rich Documents (VRDs) play a vital role in domains such as academia, finance, healthcare, and marketing, as they convey information through a combination of text, layout, and visual elements. Traditional approaches to extracting information from VRDs rely heavily on expert knowledge and manual annotation, making them labor-intensive and inefficient. Recent advances in deep learning have transformed this landscape by enabling multimodal models that integrate vision, language, and layout features through pretraining, significantly improving information extraction performance. This survey presents a comprehensive overview of deep learning-based frameworks for VRD Content Understanding (VRD-CU). We categorize existing methods based on their modeling strategies and downstream tasks, and provide a comparative analysis of key components, including feature representation, fusion techniques, model architectures, and pretraining objectives. Additionally, we highlight the strengths and limitations of each approach and discuss their suitability for different applications. The paper concludes with a discussion of current challenges and emerging trends, offering guidance for future research and practical deployment in real-world scenarios.