Documents for AI: extraction, OCR and verifiable quality

Having text does not mean having the document

An extraction can return many words and still lose the table needed for a decision. A PDF can mix digital text, images and pages with different reading orders. If search receives only continuous text, these losses are difficult to detect.

OCR is text recognition in images. Use it where needed, with defined costs and limits. A new attempt must not silently replace a structured result with a lower-quality extraction.

Preserve the original and check output by page

In a document flow I implemented, a cryptographic digest identifies the file. Processing reuses a checked output and preserves the structured document. The contract reports processed pages, missing pages and partial results. Completing the process does not mean all pages were read.

Verification compares coverage by page when a usable reference exists. It also checks reading order when position information is available. This exposes concentrated losses that a document average can hide.

Show what could not be assessed

Without a reliable reference, quality cannot receive the same conclusion as a complete comparison. The system must report this limit. Number differences need attention, but a number present only in an image can be valid. A difference signal must not become an automatic correction without context.

The application that uses the document must receive its source and reading limits. An AI answer based on partial extraction must allow checking against the original. The process must not promise that generation removes recognition errors.

Prepare the document collection for business use

Consulting can assess the collection, implement extraction and search, control reprocessing and define quality criteria. The review uses representative examples, including difficult files. The proposal defines cost, exception handling and the human review required.