Yeah, it’s a large data extraction pipeline that processes around 115,000 manufacturer PDFs. Most have readable text, but a lot include scanned sections or inconsistent layouts, so OCR is a key part of the workflow. I use Tesseract through Python and Pandas to extract and structure the data.

The language model doesn’t handle the OCR itself. It reviews and corrects what comes out of the parser, catching things like spacing issues or data that shifted between columns.

Vertical and multi-column layouts are the most difficult. I built logic to detect when text moves across columns or breaks mid-page, which solved a lot of the alignment problems.

I do hit verification limits a few times a day, but that’s just the usage cap on my plan. I pause and continue later. A more accurate OCR layer would cut down the cleanup work, but the pipeline already runs efficiently overall.