OCR (optical character recognition) turns pixels or a PDF text layer into characters and positions. AI-assisted extraction can use patterns and context to interpret which values form a transaction. Many products combine methods. The labels alone do not tell you whether a statement was extracted completely; the review process and measured results on your own layouts matter.
OCR: character reading and layout
OCR is useful when the source is image-based or when text and position must be read from a page. It can struggle with faint scans, unusual fonts, broken columns, or descriptions that wrap across lines. A character-level error can change a date or amount even if most of the page looks correct.
AI-assisted extraction: context and interpretation
AI methods can help interpret varied layouts and identify relationships among dates, narrations, debits, credits, and balances. They can also produce a confident but incorrect interpretation. A system's ability to handle a new layout does not mean every row is guaranteed accurate, and an AI model should not make a final accounting classification without the appropriate review.
Use evidence to compare them
Choose statements that represent the work you actually receive: different page counts, narration lengths, debit and credit activity, opening and closing balances, and any layout that has caused manual rework. For each tool, compare the output to the source and log:
- Whether every transaction row appears once.
- Whether dates, amounts, signs, and balances match.
- How wrapped narrations and page boundaries are handled.
- Which cases are flagged rather than guessed.
- How corrections are made and retained for review.
The practical answer is a controlled workflow
The extraction method matters, but so do the PDF, output schema, and checks around it. Preserve the source, inspect the rows, compare totals, resolve ledger assignments, and test the final file in the target accounting system. The PDF preparation checklist andconverter test plan make those comparisons repeatable.