South Korea Driver License OCR: Extract Fields with Python

South Korea document intake: what needs review

Keep Hangul names and addresses as printed rather than substituting a booking transliteration. Review date labels before normalizing values. In a production intake, keep the uploaded document associated with the correct application record before extraction. A legible name alone does not establish that the number, dates and address were captured correctly. Avoid filling missing values from a previous booking or a customer profile without recording that separate source.

Map South Korea document evidence into your backend

Check the document type, printed issuing region and date labels before using the result. Keep licence class and any visible restrictions available for review. OCR reads evidence from a card; it does not check a live driving record or determine vehicle eligibility. Send the image to the driver license API and keep extracted evidence separate from your approval decision. The same endpoint handles the submitted image; the article's country or state context does not force the API to return that jurisdiction. Compare the returned country with the source before saving the result. For coverage, pricing, and the full field list, see the driver license OCR API overview, or try a photo in the free online driver license scanner.

A reviewable intake workflow

• South Korea document intake: Keep Hangul names and addresses as printed rather than substituting a booking transliteration. Review date labels before normalizing values.
• Map fields without losing their meaning: Store document_number as text, keep surname and given_names in their own fields, and retain source images according to your document-handling policy. Date parsing failures should be reviewable instead of becoming today's date.
• Compare a resubmitted document: Keep a prior extraction and a new extraction as separate events. A change in number, name or expiry should produce a review item, not an automatic overwrite of a trusted record.
• Export only the fields your workflow uses: Map the response to your form explicitly. Keep the licence class separate from vehicle registration, insurance and employer approval.

Python request for South Korea driver license OCR

This REST example sends a local image as Base64 JSON. Run it on a backend and keep the API key in an environment variable. The printed example below is deliberately synthetic and is not a valid document-number format specimen.

Prerequisite: Python 3, pip install requests; STRUCTOCR_API_KEY set on the server

import base64
import os
import requests

with open("south-korea-driver-license.jpg", "rb") as source:
    encoded = base64.b64encode(source.read()).decode("ascii")

response = requests.post(
    "https://api.structocr.com/v1/driver-license",
    headers={"x-api-key": os.environ["STRUCTOCR_API_KEY"]},
    json={"img": encoded},
    timeout=60,
)
response.raise_for_status()
result = response.json()
if not result.get("success"):
    raise RuntimeError("Extraction was not successful; inspect the response securely")
data = result.get("data") or {}
required = ["document_number", "surname", "date_of_expiry"]
missing = [field for field in required if not data.get(field)]
# Queue incomplete records; do not log identity data in production.
print({"needs_review": bool(missing), "missing_fields": missing})

Illustrative response fields

Synthetic subset of the response for mapping purposes. Values and dates are examples, not a real scan or an assertion about document validity periods. Actual fields depend on the submitted document.

{
  "success": true,
  "data": {
    "type": "drivers_license",
    "region": null,
    "country_code": "KOR",
    "document_number": "EXAMPLE-NOT-A-REAL-ID",
    "surname": "EXAMPLE",
    "given_names": "PERSON",
    "date_of_birth": "1990-01-15",
    "date_of_issue": "2024-04-12",
    "date_of_expiry": "2029-04-12",
    "issuing_authority": null,
    "vehicle_class": null,
    "address": null,
    "extra_details": {}
  }
}