Support of Tagged PDF in the Advanced Data Extraction Technology — by OpenDataLoader PDF

1. Introduction
2. What are Tagged PDFs
3. Problems with Conventional PDF Extraction
4. A fruitful collaboration — OpenDataLoader approach based on Tagged PDF
5. Use Cases

1. Introduction

Extracting structured data from PDF documents is one of the most challenging tasks in digital document processing. Traditional PDFs were designed not for machine interpretation; they store content for visual presentation rather than logical understanding. As a result, traditional extraction tools often struggle with reading order, table structures, hierarchical relationships between document elements, and hidden text.

OpenDataLoader solves these problems through an advanced data extraction technology which, among other algorithms includes the support of Tagged PDF — a standard that fixes semantic structure directly into the document. Tagged PDF technology gives more accuracy, consistency, scalability, AI-safety in data extraction and document understanding.

2. What are Tagged PDFs

A Tagged PDF is a PDF document that includes embedded tags defining its logical structure. These tags describe how content should be interpreted and presented by assistive technologies, making the document accessible to all users.

Each tag identifies the type of content — such as headings, paragraphs, lists, tables, images, or footnotes — and stores attributes related to it. Collectively, these tags form a hierarchical structure that preserves the document’s reading order and organization.

Structurally, tagged PDFs are similar to HTML. For example, headings are enclosed in <H> tags, paragraphs in <P> tags, and images in <Figure> tags, providing a clear semantic framework for both humans and machines to understand.

Tagged PDFs maintain a clear reading order and reflect the meaning of each component. This semantic layer forms the foundation for accurate, contextual data extraction and makes Tagged PDFs ideal for automation and AI-driven processing.

Not every PDF is Tagged. Creating Tagged PDF requires special efforts from the authoring software. Most of the office applications today include the support for Tagged PDF as a special option. Our latest evaluation of PDFs in the open Web suggests that about 50% of recently generated PDFs are Tagged, and this number is increasing.

3. Problems with Conventional PDF Extraction

Most existing text extraction tools treat PDFs as a collection of coordinates rather than a structured document. This approach introduces several challenges:

Disordered Content: Text may appear out of sequence, especially in multi-column layouts.

Loss of Structure: Tables, lists, and nested sections are often flattened or misinterpreted.

Hidden or Layered Text: PDFs may include invisible layers, annotations, or overlapping text objects that interfere with accurate extraction.

Complex Layouts: Multi-column formats, tables, or embedded graphics can confuse extraction algorithms, resulting in incorrect text flow or mixed content.

Language identification in multi-lingual documents: Only the prevailing language is identified, losing the correct understanding of text in other languages.

These issues make it error-prone and computationally expensive to extract accurate, machine-readable data from complex PDFs, limiting automation and data analytics capabilities.

4. A fruitful collaboration

OpenDataLoader approach to support Tagged PDF

Hancom and Dual Lab, in partnership with the PDF Association, are working together to develop a comprehensive solution that enhances Tagged PDF usability and standardization.

OpenDataLoader PDF introduces a new approach by fully utilizing the Tagged PDF semantics if it is already present in the document and has acceptable quality. This permits reconstructing document structure more intelligently, for further AI consumption.

Through this approach OpenDataLoader PDF extraction engine combines semantic tagging, layout analysis, and AI-driven reasoning to identify relationships among text blocks, tables, and visual elements in the most efficient way.

Key components include:

The document is tagged (and the structure tree has acceptable quality)

• tagged structure parser interprets embedded PDF metadata
• veraPDF-based validator evaluates the quality of document structure tree identifying violations against the existing PDF standards such as PDF 1.7 and PDF 2.0, PDF/UA,
• well-Tagged PDF and ISO 32005 defining the schema for the PDF structure tree
• conversion of PDF structure to other machine-readable formats such as JSON, Markdown and HTML

The document is not tagged or the structure tree has low quality

• layout and object model captures spatial and logical relationships
• AI-powered inference layer refines table structure, mathematical formulas and diagrams
• OpenDataLoader transforms raw PDFs into structured, reliable datasets ready for automation and AI-driven processing.

5. Use Cases

OpenDataLoader’s Tagged PDF-based extraction can be used across multiple industries:

• Financial Services: automate the extraction of structured data from invoices, financial statements, and annual reports for real-time analysis and reporting. In a financial report, proper tags enable an AI to precisely extract the title of a balance sheet and its corresponding data cells, automating analysis without relying on error-prone heuristics.
• Legal & Compliance: rapidly parse contracts and legal documents to identify key clauses, dates, and entities, accelerating due diligence and review processes.
• Research & Academia: extract tables and figures from scientific papers and research documents, enabling automated data collection and meta-analysis.
• Enterprise Document Automation: build custom document processing workflows to convert legacy PDFs into modern, searchable, and machine-readable formats.

By ensuring accurate structure and data relationships, OpenDataLoader enables faster automation, reduces manual intervention, and increases the reliability of extracted data.

Contact us

Your interest and feedback are invaluable to us. Please explore our code, go over open issues and become a part of our growing community.

Website: opendataloader.org

GitHub: https://github.com/opendataloader-project/opendataloader-pdf

E-mail: open.dataloader@hancom.com

Why Tagged PDF Matters for AI was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.