Document-AI

It can combine optical character recognition, layout analysis, classification, and language models. Recognizing text is only one part of preserving a document’s meaning.

Key takeaways

• Preserve layout and field provenance.
• Validate meaning after extraction.
• Test varied formats and uncertain cases.

Identify whether the file already contains usable text or requires OCR. A scan can introduce recognition errors, while an existing text layer can still have incorrect reading order. Tables, columns, headers, and footnotes often require layout information to interpret correctly. Keep provenance at the field or passage level. Page numbers, bounding boxes, and original text help reviewers confirm an extracted value. Avoid flattening a table in a way that disconnects a number from its row label, unit, or qualifier. Validate extracted fields against the document and relevant relationships. A total can have the correct numerical type while containing a misplaced decimal point. An absent field should remain absent or explicitly unknown rather than being filled from a plausible pattern. Evaluate different document formats, scan quality, languages, and uncommon layouts. Define how uncertain fields reach review and how corrections are stored. Protect private documents with appropriate access, retention, and deletion controls, including any derived text and embeddings.

1. Construct a report table with a column labeled “Revenue, thousands of USD” and a row value of 250.
2. An extraction returning revenue_usd: 250 loses the scale. The interpreted amount is 250,000 USD if the column label applies to that row.
3. Preserve the raw cell, heading, and interpreted value so a reviewer can check the conversion.

This invented table illustrates why document structure matters beyond character recognition.