OCR and document layout

What it is

An image-only scanned page contains a picture of writing; some scanned PDFs also carry an OCR text layer. Optical character recognition converts the visible characters into text. Layout analysis identifies relationships such as reading order and table structure. Google’s Document AI documentation distinguishes text extraction from layout-oriented processing.

How it works

Start by identifying the document types: digital PDFs, scanned pages, mixed files and documents with complex tables. Test the chosen pipeline on examples of each type before processing a large collection.

Compare the output with the original. Check whether columns appear in the right order, table cells remain associated with their headers, and footnotes or small text have disappeared. Record source locations so corrections can be checked later.

Docling exposes confidence information on conversion results and recommends document-level grades for overall assessment; raw score calculations can change. Its documentation currently marks the table-score component as not implemented. An absent or unavailable confidence measure is not a pass.

Benefits

Useful extraction makes archives searchable and gives downstream retrieval a clearer structure. It can also make repetitive review easier by collecting uncertain passages.

Limits and risks

Readable output may contain silent omissions or plausible substitutions. A confidence grade does not prove that a name, quantity or table is correct. Review important fields even on apparently good pages, and measure performance on the actual collection instead of importing a vendor benchmark as a promise.