How does visual zone extraction handle photos, signatures, and other graphical fields?

Beyond text, the visual zone contains graphical elements, most importantly the holder's photograph, but often a signature as well, and sometimes additional visual security features like holograms or specific background patterns. Extracting these isn't a text-recognition problem the way name or address fields are.

It's a computer vision task focused on locating and cropping the correct region of the image rather than reading characters.

For the photo specifically, this typically works by using a document template that tells the software roughly where the photo sits on a given document type, then applying image processing to precisely locate the photo's boundaries, correct for any perspective distortion from the capture angle, and crop it out as a clean, standalone image. This cropped photo then becomes usable for downstream purposes, most commonly biometric face matching, where it's compared against a live selfie captured during the same verification session to confirm the person presenting the document is the same person pictured on it.

Signature extraction works similarly, locating and cropping the signature field where the document includes one, though it's used less often in fully automated verification flows since matching a signature against another sample is a much less reliable biometric check than facial comparison, and most modern identity verification relies primarily on the photo rather than the signature for this purpose.

Some documents include additional graphical security elements, like specific patterns that appear only under UV or infrared light, or holograms that shift appearance depending on viewing angle. These require specialized capture conditions, particular light sources, sometimes multiple photos taken under different lighting, rather than standard visual zone extraction, and they're generally treated as part of document authenticity checking rather than data extraction per se, since they're about verifying the document is genuine rather than pulling out identity information from it.

The quality of the original image capture matters enormously here, since a cropped photo taken from a blurry or poorly lit document image will itself be low quality, which can undermine any subsequent biometric matching regardless of how good the matching algorithm itself is.

ScanDoc extracts the photo field as a standard part of its document scanning process, using it to support downstream biometric verification when a business's workflow includes a live selfie comparison, in addition to the standard text-field extraction from both the MRZ and visual zone.