RateParse.

Rate sheet OCR for freight brokers — does it work?

OCR-based extraction works reliably for text-based and clearly formatted scanned rate sheets, but accuracy drops for low-quality scans, handwritten notes, or dense multi-column layouts — a rate sheet parser built for freight documents should validate output against a schema and flag failures rather than silently trusting every OCR result.

What OCR can and cannot do well

Why validation matters more than raw OCR accuracy

No OCR pipeline is 100% accurate on every document type. The practical question for a broker is not "is OCR perfect" but "does the tool catch its own mistakes" — a parser that validates extracted rows against an expected schema (numeric rate, valid date, recognized equipment type) and flags anything that fails is safer than one that silently stores whatever OCR produced.

RateParse's approach to OCR risk

RateParse validates extracted data against a fixed schema before storing it; if a row fails validation (whether due to OCR error or an unusual sheet layout), it retries extraction once and then flags the row for manual review rather than silently storing an incorrect or partial rate. Text-based PDFs, XLSX, and CSV files — which do not depend on OCR — parse most reliably; the current synthetic test corpus covering messy carrier formats parses at full recall, though a broker should still review flagged rows on any new document type.

Frequently asked questions

Does RateParse guarantee 100% accuracy on scanned rate sheets?

No tool can guarantee perfect OCR accuracy on every scan quality. RateParse validates extracted rows against a schema and flags failures for manual review instead of silently storing an unverified result, which limits the impact of an OCR error.

Are text-based PDFs more reliable than scanned image PDFs?

Yes — a text-based PDF is read directly without OCR and is the most reliable input type. A scanned image PDF depends on OCR quality and scan clarity, so results can vary more by document.