OCR PDF

Every AI document feature begins with the same unglamorous step: turning the file into text. A digital PDF exposes its characters directly; a scan must be read by OCR first. Whatever reaches the model is only as good as that extraction, which is why a crisp export and a crooked photograph of the same page produce noticeably different answers.

If a result looks wrong, check the extracted text before blaming the model. Missing columns, merged words and dropped diacritics almost always trace back to the source rather than the analysis.

A language model can only consider a limited amount of text at once. Long documents are therefore handled in sections, with the results combined — a reliable approach for summaries and question answering, and a weaker one for questions that require holding the entire document in mind at the same time, such as counting every occurrence of a term across four hundred pages.

Ask focused questions about specific sections rather than sweeping questions about the whole file, and you will get markedly more dependable answers.

Models produce fluent text whether or not they are certain, so a confident summary can still misstate a figure, a date or a negation — the last being especially costly in contracts, where dropping a single 'not' inverts the meaning. Treat output as a well-informed first draft.

Spot-check anything consequential against the source page, and never paste material you are not permitted to share into any AI tool, including this one. Where a document is confidential, prefer the browser-only tools that never transmit it anywhere.