Purchase order extraction looks simple until POs arrive from dozens of customers, formats, and ERPs. How to structure a pipeline that scales.
A raw LLM call extracts invoice data in a demo and fails in production. Build a Python document processing pipeline that holds up instead.
Invoice data extraction in Python: easy for one format, hard for twenty. The production approach that handles many supplier layouts.
Lab report data extraction in Python across labs with different tables, units, and column names. What reliable extraction looks like.
Scanned PDFs are images, not text, so pdfplumber comes back empty. Extract data from scanned PDFs in Python: OCR first, then extraction.
Handle PDF layout variations in Python: one extractor for many formats, instead of a separate script for every supplier invoice.
Extract tables from PDFs in Python with pdfplumber: bordered and borderless, multi-page tables, and the edge cases that break naive code.
pdfplumber vs PyMuPDF vs PyPDF2: how the three Python PDF libraries compare, with code examples and a decision framework for production use.
Schema-first PDF extraction with Pydantic: define the output schema before writing extraction code to keep pipelines maintainable.