A developer rebuilt a B2B order‑form extractor twice to compare a classic OCR‑regex pipeline with a large‑language‑model workflow. Both versions ingest scanned PDFs that contain fields like customer ID, PO number and delivery date, but the layouts vary between clients. The test PDFs mimic real‑world variance from Alpha GmbH and Beta AG, letting the author measure each approach under identical conditions in a controlled lab environment.

The rule‑based method relies on pytesseract to pull raw text, then applies handcrafted regexes for each possible label. In contrast, the LLM variant also starts with pytesseract, but feeds the OCR output to Ollama running LLaMA 3, which interprets field meaning from context. Setup steps include installing Tesseract, Poppler, and the Ollama client, then pulling the 4.7 GB model for quick iteration.

Benchmarking shows the regex pipeline crumbles when label phrasing changes, requiring new rules for each client. The LLM approach handles synonym variations without code changes, reducing maintenance overhead as document diversity grows. However, running a 4 GB model locally consumes noticeable CPU and memory, so teams must weigh flexibility against resource cost before replacing traditional pipelines for the organization.