𝗪𝗵𝘆 𝗥𝗲𝗴𝗲𝗫 𝗙𝗮𝗶𝗹𝗲𝗱 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗫𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻

𝗪𝗵𝘆 𝗥𝗲𝗴𝗲𝗫 𝗙𝗮𝗶𝗹𝗲𝗱 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗫𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻

I needed to extract data from 2,000 invoices. Each PDF had a different layout. I tried regex. It failed.

Here is why it failed:

• Small changes in spaces broke the patterns.
• Different currency formats caused errors.
• Scanned images had no text layer.
• ML models required too much data to train.

I switched to an LLM with function calling. I treated extraction as a translation problem. I turned messy text into a structured JSON object.

The process is simple:

• Get raw text from the PDF.
• Send text to the model with a schema.
• Get the JSON output.

There are a few risks:

• Cost: LLMs cost money per page.
• Speed: Each call takes a few seconds.
• Hallucinations: Models sometimes guess numbers.
• Privacy: External APIs see your data.

I fixed these by adding a validation layer. I checked if dates and numbers were real. I used local models like Llama 3.2 for private data.

Use regex for consistent data. Use LLMs for chaos. Validation is the most important part.

How do you handle messy data?

Source: https://dev.to/__c1b9e06dc90a7e0a676b/why-regex-wasnt-enough-for-data-extraction-and-what-i-used-instead-29id Optional learning community: https://t.me/GyaanSetuAi