How text recognition works, in short
To a computer, a scan is an image: colored dots, without meaning. OCR — optical character recognition — scans the image, identifies the areas that contain writing, separates lines and characters and turns them into text. On top of this text, an index is built — a structure that allows fast searching without re-reading every document. The quality of the result depends on the source: a printed document, scanned straight, at 300 dpi, is recognized almost perfectly; one photographed at an angle, with shadows, or handwriting, is recognized only partially. That's why the platform always keeps the original image alongside the extracted text — the text serves the search, the image remains the document.
Content search plus metadata filters
Text-only search returns too many results, and metadata-only search assumes someone filled it in correctly. The combination of the two is what works in practice. You start from a word or phrase that definitely appears in the document, then narrow down with the filters you already have: the time range, the department, the records-schedule category, the issuer. Results display the text snippet where the match was found, so you can quickly tell the document you're looking for apart from similar ones. The same search can be saved and reused — useful for periodic checks, where the question repeats month after month.
Metadata filled in automatically from the recognized text
The most time-consuming part of digitizing an archive isn't scanning, it's filling in metadata. OCR cuts this work down significantly: common fields — the document's date, number, issuer, amount — can be extracted automatically from the recognized text. They're proposed, not imposed: the operator confirms or corrects them, and corrections improve the results that follow. For large batches of documents of the same type, extraction templates can be defined once and applied to the whole batch, turning a task of weeks into one of days.
What OCR can't do, and how to avoid surprises
It's only honest to state the limits too. Handwriting is recognized inconsistently, and manually filled-in forms generally remain a problem. Documents with stamps overlapping the text, low-resolution scans or folded pages produce recognition errors. In practice, this means a searched word may be missing from the index even though it's on the paper. The recommendation is simple: for critical categories, fill in a few key metadata fields by hand, so retrieval doesn't depend solely on the recognized text, and scan at a decent quality from the start — rescanning costs more than scanning well the first time.
