B2B Document Extraction: Rule-Based vs. AI-Powered Approaches
This article explores a practical comparison between two approaches for extracting data from B2B order documents: a traditional rule-based method using pytesseract and a modern LLM-based method powered by Ollama and LLaMA 3. Both were built and tested on the same realistic B2B order scenario to highlight differences in accuracy, flexibility, and implementation effort.
What is the core comparison in this article?
The article compares two different strategies for building a B2B document extractor. The first relies on hard-coded rules and pytesseract (an OCR engine) to parse PDF order forms. The second leverages a large language model (LLM) via Ollama and LLaMA 3, which interprets document content more flexibly. The same set of realistic B2B order PDFs was used for both approaches, allowing a direct side-by-side evaluation of how each handles data extraction tasks.
What tools were used for the rule-based approach?
The rule-based approach employed pytesseract, a Python wrapper for Google's Tesseract OCR engine, to convert PDF images into machine-readable text. After OCR, custom scripts with predefined patterns (e.g., regex rules) extracted specific fields like order number, customer name, line items, and totals. This method required thorough analysis of the document layout to craft precise extraction rules.
What tools were used for the LLM-based approach?
The LLM-based approach used Ollama as a local inference server to run LLaMA 3, an open-source large language model. The PDFs were first converted to text using pytesseract, and then the raw text was passed to LLaMA 3 with a prompt instructing it to extract structured data. This eliminated the need for manual rule crafting, as the model could understand context and adapt to minor variations in document formatting.
How did accuracy compare between the two methods?
Accuracy depended heavily on document consistency. The rule-based approach achieved near-perfect extraction on well-structured, predictable forms but failed when slight layout changes occurred (e.g., rotated text, different tables). The LLM-based approach handled variations more gracefully, correctly extracting data from about 20% more documents, though occasional hallucinations or missing fields occurred when the text was too noisy. Overall, the LLM offered higher robustness at the cost of slightly lower precision on perfectly formatted documents.
What were the implementation efforts and costs?
Building the rule-based extractor required several days of manually inspecting PDFs, writing and testing regex patterns, and handling edge cases. The LLM-based approach, in contrast, needed only basic prompt engineering and could be set up in a few hours. However, the LLM approach demands computational resources (Ollama running LLaMA 3 locally on a GPU) and per-query latency, while the rule-based method runs quickly on any machine. For low-volume or stable documents, rules were more cost-effective; for high-variety documents, the LLM saved development time.
When should a team prefer rules over LLMs for document extraction?
Teams should prefer rule-based extraction when document formats are fixed and clean, such as standard purchase orders from a single supplier. This approach is fast, cheap, and easy to debug. It also works well when OCR quality is high and there are no unusual layouts. If the number of document templates is stable and you need deterministic, auditable extraction, rules are the way to go.
When does an LLM-based approach become the better choice?
An LLM-based approach shines when dealing with diverse or unpredictable document formats, such as order forms from many different clients. It adapts to missing fields, inconsistent tables, or even handwritten entries (via OCR) without needing code changes. The trade-off is higher computational cost and non-deterministic output. Teams that value speed of deployment and flexibility for evolving document types will benefit from using an LLM like LLaMA 3 with Ollama.