I Built the Same B2B Document Extractor Twice: Rules vs. LLM

2026-05-13 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, long

Summary

This article compares two methods for extracting structured data from B2B order forms, which often vary significantly in layout despite containing similar information like customer ID, purchase order number, and delivery date. The first approach uses a traditional rule-based system combining `pytesseract` for Optical Character Recognition (OCR) and regex rules. The second approach integrates `pytesseract` with an LLM, specifically `LLaMA 3` running locally via `Ollama`. The traditional method struggles with layout variations, failing to extract data from a second, differently formatted PDF, whereas the LLM-based approach successfully extracts and normalizes data from both layouts by understanding semantic context. The comparison highlights that while traditional methods are faster and more explainable for stable, standardized documents, LLMs offer superior flexibility and reduced maintenance effort for environments with high document variability, albeit with increased infrastructure demands and inference times.

Key takeaway

For operations professionals managing document processing with diverse B2B order forms, consider adopting an LLM-based extraction pipeline using tools like `Ollama` and `LLaMA 3`. While traditional regex is suitable for stable, standardized documents, an LLM approach significantly reduces maintenance effort and improves accuracy when dealing with numerous, varied layouts, despite requiring more robust infrastructure and potentially longer inference times. Evaluate your document variability and throughput needs to determine the optimal strategy.

Key insights

LLMs offer superior flexibility for varied document layouts compared to rigid regex rules.

Principles

• Traditional OCR struggles with layout variability.
• LLMs interpret semantic context for data extraction.
• System complexity shifts from rules to infrastructure with LLMs.

Method

The method involves converting PDFs to images via `pdf2image`, performing OCR with `pytesseract`, and then either applying regex rules or sending the extracted text to a local LLM (`LLaMA 3` via `Ollama`) for structured data extraction.

In practice

• Use `pytesseract` for OCR on scanned PDFs.
• Run `LLaMA 3` locally with `Ollama` for flexible extraction.
• Generate test PDFs with `fpdf2` for consistent comparisons.

Topics

• B2B Document Extraction
• LLM-based Data Extraction
• Regex Data Extraction
• Optical Character Recognition
• pytesseract

Code references

• Sari95/ocr-pdf-extraction-regex-vs-llm
• UB-Mannheim/tesseract
• oschwartz10612/poppler-windows

Best for: AI Engineer, MLOps Engineer, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.