# Fixing a name or field that was read wrong

Source: https://help.didit.me/verification-sessions/correct-extracted-data
Updated: 2026-10-12

Support can't edit a misread name or field, but you can fix it three ways: the user data review step, the update-data API, or resubmitting the ID step.

Didit support **cannot edit the values** on your session. You can, three ways:
let the person confirm and correct the data in the flow (**User data review**),
patch the fields yourself in the console (**More actions → Edit KYC data**) or
with the **update-data API**, or **request a
resubmission** of the ID step after tightening the image quality bar. Nearly every
misread comes from a poor capture.

[Video: User data review: let users fix OCR mistakes - Didit Academy](https://www.youtube.com/watch?v=JPFGP24BLHI)

A name with one letter wrong, a surname that "did not load", a session whose name shows as **unknown**, or an address that landed in the name field are all the same problem: the extraction ran on an image that was not good enough, or on a document side the model does not read. The data in the session is what the model read, and Didit does not change customer data by hand: your team owns the record, and any change has to be traceable to you.

## First, understand why it happened

Open the session and look at the document images and the warnings:

- **Blurry, dark, glared or partially cropped image.** The most common cause by far. The session usually carries a low image quality score or an `IMAGE_TOO_BLURRY` warning. See [fixing document upload and reading problems](https://help.didit.me/documents-coverage/document-upload-problems).
- **Uploaded file instead of a live capture.** Uploads are often screenshots or compressed photos and misread far more often than camera scans.
- **A non-Latin script.** Names in Cyrillic, Arabic, Greek, Chinese or Korean can come back scrambled or empty if the workflow's **Character format** setting doesn't match what you expect (original script versus Latin transliteration).
- **A document side the model doesn't read yet.** Some national IDs carry the data on the back in a layout Didit has not been trained on. In that case the front is read and the back is accepted but not extracted; send a specimen to support so the mapping can be added.

## Three ways to fix it

1. **Let the person correct it in the flow (best for new sessions)**

   In the workflow's ID verification step, under **Advanced**, turn on **User data review**: after the capture, the person sees the extracted fields and can correct reading mistakes before the result is decided. Edits to the **Critical fields** you list (name, date of birth, document number) take the action you choose - review or decline - so a correction is never silent. This fixes the problem at the source and needs no work from your team.

2. **Patch the session yourself (for an existing session)**

   In the console, open the session and choose **More actions → Edit KYC data** (or **Edit proof of address** for address fields), correct the fields and save. Developers can do the same with `PATCH /v3/session/{session_id}/update-data/`, sending only the fields you want to change: `first_name`, `last_name`, `date_of_birth`, `document_number`, `expiration_date`, `issuing_state` and the other extracted fields. `full_name` is recomputed for you. The session must be **Approved, Declined, In review or KYC Expired**, the change fires a `data.updated` webhook, and the audit trail records which fields changed and who changed them. It is a paid call. There are matching endpoints for proof-of-address fields and for KYB registry data. Reference: [update session data](https://docs.didit.me/sessions-api/update-data).

3. **Ask for a new capture (when the image is the problem)**

   If the image is too poor to trust any of the fields, request a **resubmission** of the ID verification step: the person receives the same link and repeats only that step. Before you do, raise the **Minimum image quality** slider in the step and consider enabling the **Image capture review screen**, so the retake can't be as bad as the first attempt. See [letting someone try a verification again](https://help.didit.me/verification-sessions/retry-a-verification).

> **Note:** Resubmitting the ID step **replaces** the document images and re-runs the checks
> that depend on them (face match, AML name screening, database validation) at their
> usual price. Patching a field with the API does **not** re-run anything: if the
> wrong name already produced an AML or registry result, run that check again with
> the corrected data - the standalone APIs exist for exactly this.

## What patching does not do

- It does not change the **status** of the session. A declined session with a corrected name is still declined until your team decides otherwise.
- It does not re-verify the document. The correction is your assertion, recorded as such in the audit log.
- It does not apply retroactively to other sessions of the same person.

## Stopping it from recurring

- Raise **Minimum image quality** in the ID verification step and turn on the **Image capture review screen** so people confirm the photo before it is uploaded.
- Prefer **camera scan** over file upload for production traffic.
- Set **Character format** deliberately for markets with non-Latin scripts.
- If one document type misreads consistently, send two or three anonymised specimens to support so the model can be updated.