AI document parser: extract key figures carefully
Extraction needs labels and context as well as numbers.
Define the extraction schema
List the fields you need before processing the document: raw value, label, unit, reporting period, source page and confidence or review status. Decide how missing values will be represented. An empty field, a printed dash and a numeric zero can mean different things; do not convert them to the same value just to make a table easier to load.
Keep raw and normalized values separate
Preserve the source string before converting dates or amounts. Record any normalization rule alongside the extracted output. For tables, retain row and column headings plus relevant footnotes. If a figure is an estimate or a subtotal, carry that label forward so downstream calculations do not treat it as an observed total.
Example: avoid a locale assumption
Illustrative source value “1,250” appears without a locale or unit. Do not decide solely from punctuation whether the comma separates thousands or decimals. Inspect neighboring values, headings and document conventions. Store the raw string and mark the normalized amount unresolved until there is enough evidence; an apparently tidy number can be a large extraction error.
Validate before using the dataset
Compare a sample of extracted fields directly with the original page and inspect every flagged field. Reconcile totals only where the source says the components are additive. Investigate duplicated rows, missing periods and unit changes before reporting aggregate results. Keep the source reference with the final data so a correction can be traced without repeating the entire extraction.
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