Developer Ditches Regex and OCR for AI to Extract Data from 500 PDF Invoices

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A software developer spent three days struggling to parse 500 PDF invoices with inconsistent layouts using regex patterns, OCR tools, and rule-based parsers, none of which proved reliable across all documents. Each approach failed when encountering new vendors, merged table cells, or varied label formats such as 'Total Due' versus 'Amount Total'. The developer then shifted strategy by treating invoice extraction as a structured AI generation task, feeding raw PDF text to a large language model and prompting it to return data in a defined JSON schema. PyMuPDF was used to extract raw text from each PDF, which was then sent via HTTP to an LLM API endpoint supporting JSON output mode. The author notes the technique is model-agnostic and can work with OpenAI, Anthropic, or locally hosted models that support function calling or JSON mode.

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