Parsing Multi-Page Documents Without Losing Context: How Lettria Solves a Real Workflow Problem

The Challenge

Most business-critical documents aren't neatly packaged into one page. Whether it's an insurance contract, a financial report, or a regulatory filing, real-world documents often span dozens or hundreds of pages.

And that's where the problems start.

Traditional parsing systems treat documents page by page, as if each page lives in isolation. In practice, this means:

• Tables are cut in half and misaligned
• Headers and footers are duplicated and treated as content
• Context is lost at every page break

For teams working in insurance, legal, or finance—where accuracy and traceability are mandatory—this isn’t just inconvenient. It creates costly errors and manual rework.

This article walks through how Lettria tackles these issues head-on with a document intelligence approach that sees the document as a whole, not a sequence of pages.

Why Multi-Page Layouts Break Traditional Parsing

Parsing tools built for clean, academic PDFs fail when confronted with real-world business documents. The complexity usually comes from:

• Fragmented Content
• A table that starts at the bottom of one page and continues on the next is often treated as two separate elements, breaking downstream data extraction.
• Redundant Headers and Footers
• Page headers (e.g., company name, section title) and footers (e.g., page numbers, confidentiality notices) are repeated identically on every page. Parsing tools can’t always tell that these are not part of the main content.

Keep the document structure across pages.

• Keep the document structure across pages.
• Context is read as a continuous flow, so the entire content of a chapter is linked to it within the document tree structure.e

Lettria’s Approach: A Document Graph, Not a Stack of Pages

Lettria processes documents using a hybrid approach combining deep learning and rule-based logic to extract and structure document content. Instead of working page by page, the system reconstructs the document as a single, connected structure.

This allows:

• Semantic continuity across pages
• Paragraphs, tables, and bullet lists are reconnected, even if they span multiple pages.
• Header/footer detection and filtering
• Repeated sections are detected, and then you can choose to keep them, filter some of them based on a regex, or remove them.
• Cross-page reference resolution
• Lettria preserves the tree structure (cross-pages) to ensure that each content is linked to the correct title.

The result: your document is parsed as it was intended to be read—as a single, coherent narrative.

Reconstructing What Was Meant, Not What Was Printed

Lettria’s pipeline is built to reassemble what the document means, not just how it was formatted. Here’s how it handles common multi-page issues:

• Split tables
• Tables that are broken across pages are detected and merged into one complete structure, preserving row continuity and column alignment.
• Repeated section labels
• The tree structure allows linking elements across pages.

Example: Parsing a Multi-Page Insurance Contract

Let’s take a 40-page property insurance contract.

Before Lettria

• Tables listing coverage limits are split across two pages, interpreted as separate entities.
• “Definitions” section is repeated as a header on every page, bloating the extracted content

After Lettria

• All split tables are reassembled into complete, navigable data structures
• Headers and footers are removed, with content fully de-noised

The contract is now machine-readable and usable for downstream tasks like clause classification, risk scoring, or comparison across providers.

Examples

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Output via the Markdown interpreter:

The table is merged into one.

Exporting Clean, Traceable Outputs

Once processed, Lettria exports as a unified JSON, markdown, or XML(tree structure) that reflects the document as if it were written on one logical page.

• Clean structure: no split content, no duplicate labels
• Traceability: Every extracted element is linked to its original page and coordinates, allowing auditability and compliance checks
• Ready for automation: JSON can be fed directly into RPA tools, contract intelligence engines, or BI dashboards

Use Cases Across Regulated Industries

This capability is particularly relevant for:

• Claims Processing
• Quickly extract and validate key information from long-form insurance documents, even if tables or terms are split across pages.
• Contract Review
• Automate clause extraction and version comparison across lengthy agreements without risking missed content due to page breaks.
• Regulatory Audits
• Ensure all required sections are accounted for and linked, even when documents follow inconsistent templates or structures.

Final Thoughts

Multi-page documents are a reality in any regulated industry. Most tools weren’t built to handle them. Lettria is.

Our graph-based approach makes long-form documents usable: connected, clean, and ready for automation.

Want to see how it works on your documents?

Request a demo to see how Lettria can handle your most complex parsing challenges.