People connect position, color, arrows, labels, and notes when reading a chart. A PDF may not preserve those connections as structured data. A page can look like one table while its extracted text places headings, values, and footnotes in a different order.
Visual order may differ from reading order
Multi-column layouts, floating text boxes, and image-based tables can produce an internal sequence unlike the visible page. A heading may be joined to values on another page, or a footnote may interrupt the main paragraph.
Scanned pages add optical character recognition. Small type and low contrast can introduce another layer of error.
A table is a set of relationships
Understanding a figure requires row and column labels, units, time periods, denominators, and exceptions. Extracting numbers alone removes those relationships. Merged cells and color-only legends create the same risk.
Charts also need axis context, comparison periods, and sources. Without them, a system may describe the shape while missing the meaning.
How can a document preserve the context?
- Align visible headings with the actual document hierarchy.
- Keep row and column labels and units as text.
- Add a short description of the chart’s main relationship.
- Repeat essential figures from images in accessible body text.
- Record the source and reference date near the claim.
- Test likely questions against the original document.
Feat AI publicly describes structuring PDFs, data, and web content for AI understanding and citation. This article deliberately excludes the accuracy, latency, API-call, and token figures on that page because the underlying experiment report is not available here.
Structure improves verification, not certainty
Better structure does not guarantee a correct answer or citation by any AI service. Keep the original source, named review responsibility, and factual checks for material claims.