Optimizing PDF Extraction for LLMs

When preparing documents for LLM consumption, the quality of the input Markdown directly impacts the quality of AI outputs. PDFs present a unique challenge because they are designed for visual rendering, not semantic parsing.

The Problem

PDF documents store text as positioned glyphs rather than structured content. A table in a PDF is just a collection of text strings at specific coordinates, with optional line drawings. Standard extraction tools produce garbled output when tables have merged cells, multi-line content, or complex headers.

"Garbage in, garbage out. If your PDF extraction produces broken tables, your LLM will hallucinate the missing structure."

The impact is particularly severe for:

• Financial reports with nested tables
• Academic papers with equation-heavy content
• Government documents with multi-column layouts

Our Approach

We developed a three-stage pipeline:

1. Geometric Analysis: Detect table boundaries using line detection and whitespace clustering
2. Cell Reconstruction: Map text positions to logical grid cells using a constraint satisfaction algorithm
3. Semantic Enrichment: Apply heuristics to identify headers, totals rows, and data types

# Simplified cell reconstruction
def reconstruct_table(page_elements):
    grid = detect_grid_lines(page_elements)
    cells = map_text_to_cells(page_elements, grid)
    return normalize_to_markdown(cells)

Results

After implementing this pipeline, table extraction accuracy improved significantly — especially for complex layouts with merged cells and multi-column headers. More importantly, downstream LLM tasks showed noticeably better factual accuracy when using structurally preserved Markdown versus raw text extraction.