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So you want to parse a PDF?

A technical discussion on the challenges of PDF parsing and why computer vision approaches often work better than traditional metadata-based methods.
Signal Editorial Team
AI-generated and human-reviewed news meta-commentary
3 minute read

So you want to parse a PDF?

PDF parsing presents one of computing’s most deceptive challenges. What appears straightforward—extracting text from a document—quickly becomes a journey through format inconsistencies, broken implementations, and architectural decisions that prioritize visual fidelity over data extraction.

The PDF Parsing Problem

PDFs store content as drawing instructions, not structured data. Unlike HTML or XML, PDFs describe where to place each character on a page rather than organizing content semantically. This fundamental design creates parsing challenges that traditional text extraction methods struggle to solve reliably.

The format’s complexity stems from its origins as a print-ready document standard. PDFs must render identically across different systems, leading to a specification that accommodates countless edge cases and vendor-specific implementations.

Why Computer Vision Approaches Win

Industry practitioners increasingly abandon traditional PDF parsing for computer vision solutions. Companies processing millions of documents monthly report better results by converting PDFs to images, then applying layout understanding models and OCR.

This approach works because:

  • Visual consistency: PDFs render reliably as images across different generators
  • Layout preservation: Computer vision models understand spatial relationships between elements
  • Unified processing: One pipeline handles both text-based PDFs and scanned documents
  • Reduced complexity: Avoids the maze of PDF specification edge cases

Traditional parsing requires handling character positioning, font encoding issues, and reading order reconstruction. Computer vision sidesteps these problems by processing the visual output directly.

The Technical Reality

PDF parsing fails in predictable ways:

Character encoding problems: PDFs often use custom font encodings where ‘A’ might not map to ASCII 65. Ligatures like “ff” frequently split into separate characters during extraction.

Reading order chaos: Text appears in PDFs based on drawing order, not reading order. A sentence might be scattered across different sections of the file, requiring complex algorithms to reconstruct proper sequence.

Structural inconsistencies: Tables, columns, and paragraphs exist only visually. Parsers must infer these structures from character positions, leading to frequent failures with complex layouts.

Metadata unreliability: PDF metadata often contradicts visual content. Form fields may have nonsensical names, and embedded text might not match what renders on screen.

Implementation Approaches

Direct parsing works for controlled environments with known PDF generators. Libraries like MuPDF and Poppler can extract text efficiently when documents follow predictable patterns. This approach requires handling font mappings, coordinate systems, and text reconstruction algorithms.

Computer vision pipelines convert PDFs to high-resolution images, then apply:

  • Layout detection models to identify text blocks, tables, and figures
  • OCR engines for text recognition
  • Specialized models for complex elements like mathematical formulas

Hybrid approaches combine both methods, using direct parsing where possible and falling back to vision-based processing for problematic sections.

Common Mistakes

Developers often underestimate PDF parsing complexity:

  • Assuming text extraction is straightforward
  • Ignoring font encoding issues
  • Failing to handle reading order reconstruction
  • Not accounting for scanned documents mixed with native PDFs
  • Overlooking the need for robust error handling

Making the Right Choice

Choose computer vision approaches when:

  • Processing PDFs from diverse sources
  • Accuracy requirements are high
  • Documents contain complex layouts or tables
  • Budget allows for GPU-based processing

Stick with traditional parsing when:

  • Working with known PDF generators
  • Processing simple, text-heavy documents
  • Computational resources are limited
  • Real-time processing is required

Next Steps

Start with existing solutions before building custom parsers. Services like Tensorlake, Nutrient, and open-source tools like Docling provide battle-tested implementations. For custom solutions, begin with computer vision approaches—they handle more edge cases and provide more consistent results across diverse document sources.

The PDF parsing landscape continues evolving as machine learning models improve. What remains constant is the format’s inherent complexity and the need for robust, flexible approaches to extract meaningful data from these ubiquitous but challenging documents.