"We already have an OCR tool. Why do we need something like Parseloom?" This is a fair question. Understanding the answer requires being clear about what OCR does and what it doesn't do.
What OCR does
Optical character recognition converts an image of text into a sequence of characters. Given a scanned invoice, OCR returns a string of text. If the scan is clean and the font is standard, it returns accurate characters. That's the job.
OCR doesn't know what the characters mean. It doesn't know that "4,182.00" in the lower-right quadrant of an invoice is the total amount due, while "975.00" repeated four times in a table column is a unit price, and "3,900.00" in the rightmost table column is a line-item total. It returns all of those as character strings in a sequence. The distinction between them is lost.
What structured extraction does
Structured extraction takes the output of OCR (or a native PDF's embedded text layer) and maps specific text values to named fields. "4,182.00" becomes total_amount: 4182.00. "975.00" in the unit price column becomes a value in line_items[].unit_price. The document becomes a structured record you can post to an ERP without a human reading and keying.
The extraction model understands document structure: which text blocks are labels versus values, how table columns correspond to field types, what position and context cues indicate which field is being referred to. That understanding is what OCR alone doesn't provide.
When the distinction matters most
For documents with consistent, simple layouts (a single-page document from one vendor type), regex patterns on OCR output can approximate structured extraction. Many teams have built these - per-vendor regex scripts that work until the vendor changes their invoice template.
The distinction matters most when layout varies. A 50-vendor invoice set with 50 different layouts requires 50 different regex scripts, or a model that understands layout generically. Generic layout understanding is what Parseloom's extraction model is trained to do.
It also matters for table extraction. Regex works for fixed-position fields. For line-item tables where the number of rows varies and column positions aren't fixed, regex breaks down. Structured extraction handles variable-length tables as a first-class case.
So when is OCR alone sufficient?
OCR alone is sufficient when you need verbatim text capture with no field assignment, when you have a single document type from a single source that never changes layout, or when a human will read the OCR output and key the data anyway. If that's your workflow, OCR is the right tool and adding a structured extraction layer doesn't add value. If your goal is automatic posting to an ERP with no human keying step, OCR alone doesn't get you there.