I Built the Same B2B Document Extractor Twice: Rules vs. LLM
📰 Towards Data Science
Compare rule-based and LLM-based approaches for B2B document extraction using pytesseract, Ollama, and LLaMA 3
Action Steps
- Build a rule-based PDF extractor using pytesseract
- Implement an LLM-based approach with Ollama and LLaMA 3
- Compare the performance of both approaches on a realistic B2B order scenario
- Evaluate the accuracy and efficiency of each method
- Choose the best approach based on the comparison results
Who Needs to Know This
Data scientists and software engineers can benefit from this comparison to choose the best approach for their document extraction tasks
Key Insight
💡 LLM-based approaches can be more accurate and efficient than rule-based methods for document extraction tasks
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🤖 Compare rule-based vs LLM-based document extraction using pytesseract, Ollama & LLaMA 3
Key Takeaways
Compare rule-based and LLM-based approaches for B2B document extraction using pytesseract, Ollama, and LLaMA 3
Full Article
A practical comparison between rule-based PDF extraction using “pytesseract” and an LLM-based approach with “Ollama” and “LLaMA 3”, based on a realistic B2B order scenario. The post I Built the Same B2B Document Extractor Twice: Rules vs. LLM appeared first on Towards Data Science .
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