Graph Neural Networks for Document Layout Analysis and Data Accuracy

In a world increasingly driven by automation, the battle for operational efficiency hinges on one critical factor: the accuracy of extracted data. For industries from fintech to healthcare, inaccurate document processing introduces errors, rework, compliance risks, and financial losses. Yet, traditional OCR models have hit a ceiling when it comes to processing the messy, crumpled, and skewed realities of physical receipts.

At Veryfi, we believe the future demands more than incremental improvements. It demands a reinvention of how machines understand documents. Our latest advancement — a graph-based document layout analysis model powered by Graph Neural Networks (GNNs) and quadrilateral geometries — is designed to meet that future head-on.

Why Traditional OCR Models Fall Short

Most OCR extraction systems today represent documents as simple grids of rectangles. Each text fragment is boxed, and relationships between them are inferred primarily by proximity. This method works well for pristine, neatly scanned documents. But real-world receipts — especially those captured via mobile devices in dynamic environments — are rarely so cooperative.

Highly skewed, crumpled, or distorted receipts often produce OCR outputs where rectangles overlap, misalign, or poorly represent the text's true flow. Worse, without context, legacy models struggle to distinguish meaningful groupings of text (e.g., line items, totals) from visual noise.

The result? Fragmented data extraction, loss of critical fields, and a manual cleanup burden that undermines the promise of automation.

Veryfi’s Breakthrough: Graph-Based Dewrapping with Contextual Intelligence

Our new model redefines document representation:

• Quadrilaterals Over Rectangles: In earlier iterations, we followed the conventional approach of using rectangular bounding boxes to represent OCR fragments. But real-world receipts — skewed, rotated, or captured under poor lighting — exposed the limitations of this method. By adopting quadrilateral representations, our model now captures text geometry with far greater fidelity, enabling more accurate line reconstruction in complex layouts.
• Graph Neural Networks (GNNs): Every text fragment becomes a node in a graph. Relationships (edges) between nodes are not hardcoded; they are learned. The GNN accumulates contextual signals from neighboring nodes to intelligently reconstruct text lines and logical groupings.
• Context-Aware Line Building: By embedding local and global context into the line assembly process, the model moves beyond "nearest neighbor" logic, enabling a deeper understanding of document structure even under severe distortion.

This approach dramatically improves our ability to "unwrap" a receipt into its true, human-readable form — no matter how skewed, curved, or folded it might be. The example below illustrates how graph-based document layout analysis works in 3 steps: 

(1) Raw image with OCR fragment overlays

(2) Quadrilateral representations of text fragments, showing distortion; 

(3) Final structured layout reconstructed by Veryfi’s Graph Neural Network, accurately grouping related text into coherent lines.

Real-World Impact: Precision Under Pressure

While traditional evaluation metrics like classification accuracy only tell part of the story, internal testing on complex, high-failure receipts is revealing. In scenarios where old models failed completely, our new approach succeeds in reconstructing accurate structures in over 90% of cases.

This breakthrough has profound business implications:

• Higher Automation Rates: More receipts processed straight-through, without human intervention.
• Better Fraud Detection: Stronger structural reconstruction leads to better anomaly detection and fraud prevention.
• Reduced Rework and Costs: Fewer extraction errors mean lower operational costs, faster cycle times, and improved customer experiences.
• Broader Use Case Coverage: Handles challenging verticals like Field Services, Travel & Expense Management, Construction, and Consumer Loyalty Programs where document quality often suffers.

In short, the model delivers precision where it’s needed most: the messy, high-friction edge cases that define real-world operations.

Beyond Receipts: A New Paradigm for Document Understanding

At Veryfi, we see this innovation not as a one-off upgrade, but as a foundational step toward the next generation of Intelligent Document Processing (IDP).

Graph-based document modeling opens possibilities far beyond receipts:

• Multi-page Invoice Reconstruction
• Medical Claim Unwrapping
• Expense Reports with Attachments
• Insurance Documentation Parsing

As documents become more complex and submission methods more mobile-first, the need for flexible, context-aware extraction will only grow. Quadrilaterals, graphs, and GNNs are how Veryfi is preparing customers for that future today.

Why Veryfi Leads This Evolution

Veryfi's advantage is not theoretical. It is battle-tested:

• Millions of real-world documents used to train and validate models.
• Specialization in mobile capture — understanding that a receipt photographed in a truck cab or a crowded cafe is very different from a flatbed scan.
• SOC 2 Type 2, GDPR, HIPAA compliance baked into every aspect of data handling.
• Developer-centric platforms with easy API and SDK integrations.

While other providers adapt generic models for document tasks, Veryfi engineers purpose-built solutions that address the deepest friction points of real-world automation.

Conclusion: Redefining Accuracy, Reimagining Possibilities

The future of intelligent automation belongs to those who can extract accurate data from imperfect, real-world inputs. Veryfi’s graph-based dewrapping model marks a critical leap forward in making that future accessible today.

We invite forward-thinking organizations to join us at the frontier of intelligent document automation.

Ready to experience next-generation receipt processing? Start your Veryfi trial today.