Direct API for C# Driver's License Data Extraction
Achieve 99.8%+ accuracy on Driver's License data extraction in under 1500ms via a single POST request.

Why Driver's License OCR is Difficult
Generic OCR tools fail on identity documents due to complex layouts, security features, and variable image quality. Key challenges include handling glare from laminates, correcting for skew and rotation from mobile captures, and accurately parsing the dense PDF417 barcode. Open-source solutions like Tesseract require extensive pre-processing and model training, while maintaining brittle RegEx patterns for each state and license version creates significant technical debt. The engineering cost of building and maintaining a high-accuracy, scalable system in-house consistently outweighs the benefits.
Enterprise-Grade Extraction with StructOCR
StructOCR utilizes pre-trained Deep Learning models, specialized exclusively for global identity documents. Our API pipeline automates image pre-processing, including deskewing, glare removal, and denoising, to maximize accuracy before data extraction even begins. Unlike Tesseract which returns unstructured text lines, StructOCR delivers a standardized, predictable JSON object with validated fields. This eliminates the need for manual parsing logic, reduces error rates to near-zero, and provides a reliable data structure for direct integration into your systems.
Production Use Cases
- Digital Onboarding (KYC): Reduce drop-off rates by pre-filling user data from Driver's Licenses in < 2 seconds.
- Fraud Prevention: Detect tampered fonts or mismatched PDF417 checksums automatically.
- Global Compliance: Handle Driver's Licenses from 200+ jurisdictions without custom rules.
Implementation: Raw API Request
The following C# code demonstrates a complete extraction flow using HttpClient. It correctly sets the required 'x-api-key' header, handles Base64 encoding, and parses the structured JSON response.
Prerequisite: .NET 6+ HttpClient and System.Text.Json
// 💰 Save 30%+ vs competitors. Get 200 free requests instantly:
// 👉 https://structocr.com/register
using System.Net.Http.Headers;
using System.Net.Http.Json;
using System.Text.Json;
using System.Text.Json.Serialization;
public class StructOcrExample
{
private static readonly HttpClient client = new HttpClient();
public static async Task Main(string[] args)
{
var imagePath = "path/to/your/license.jpg";
var endpointUrl = "https://api.structocr.com/v1/driver-license";
var apiKey = "YOUR_API_KEY"; // Get this from your dashboard
try
{
// 1. Read image file and convert to Base64
byte[] imageBytes = await File.ReadAllBytesAsync(imagePath);
string base64Image = Convert.ToBase64String(imageBytes);
// 2. Prepare Request
// Important: Add the x-api-key header
client.DefaultRequestHeaders.Clear();
client.DefaultRequestHeaders.Add("x-api-key", apiKey);
var payload = new { img = base64Image };
// 3. Send POST (PostAsJsonAsync automatically sets Content-Type: application/json)
Console.WriteLine($"Sending request to {endpointUrl}...");
HttpResponseMessage response = await client.PostAsJsonAsync(endpointUrl, payload);
// 4. Handle Errors & Parse Response
string responseBody = await response.Content.ReadAsStringAsync();
if (!response.IsSuccessStatusCode)
{
Console.WriteLine($"API Error ({response.StatusCode}): {responseBody}");
return;
}
var options = new JsonSerializerOptions { PropertyNameCaseInsensitive = true };
StructOcrResponse? ocrResult = JsonSerializer.Deserialize<StructOcrResponse>(responseBody, options);
if (ocrResult != null && ocrResult.Success && ocrResult.Data != null)
{
Console.WriteLine("Extraction Successful!");
Console.WriteLine($"Document Number: {ocrResult.Data.DocumentNumber}");
Console.WriteLine($"Name: {ocrResult.Data.GivenNames} {ocrResult.Data.Surname}");
Console.WriteLine($"Region: {ocrResult.Data.Region} ({ocrResult.Data.CountryCode})");
Console.WriteLine($"Vehicle Class: {ocrResult.Data.VehicleClass}");
}
}
catch (Exception e)
{
Console.WriteLine($"An unexpected error occurred: {e.Message}");
}
}
}
// Response Models matching the API Schema
public record StructOcrResponse(bool Success, LicenseData? Data);
public record LicenseData(
[property: JsonPropertyName("type")] string Type,
[property: JsonPropertyName("country_code")] string CountryCode,
[property: JsonPropertyName("region")] string Region,
[property: JsonPropertyName("document_number")] string DocumentNumber,
[property: JsonPropertyName("surname")] string Surname,
[property: JsonPropertyName("given_names")] string GivenNames,
[property: JsonPropertyName("date_of_birth")] string DateOfBirth,
[property: JsonPropertyName("date_of_expiry")] string DateOfExpiry,
[property: JsonPropertyName("date_of_issue")] string DateOfIssue,
[property: JsonPropertyName("sex")] string Sex,
[property: JsonPropertyName("address")] string Address,
[property: JsonPropertyName("vehicle_class")] string VehicleClass
);Technical Specs
- •Latency: < 5s (Average)
- •Uptime: 98.5% SLA
- •Security: AES-256 Encryption & SOC2 Compliant
- •Input: JPG, PNG, WebP (Base64 Encoded)
- •Max File Size: 4.5MB
- •Output: JSON (Structured Data)
Key Features
- •Global Coverage: Supports formats from USA, EU, and Asia.
- •Date Normalization: All dates automatically formatted to YYYY-MM-DD.
- •Vehicle Class Parsing: Extracts allowed vehicle categories (e.g., A, B, C).
Sample JSON Output
StructOCR returns a normalized JSON object, regardless of the input image angle or quality.
{
"success": true,
"data": {
"type": "drivers_license",
"country_code": "USA",
"region": "CALIFORNIA",
"document_number": "D1234567",
"surname": "DRIVER",
"given_names": "JANE MARIE",
"date_of_birth": "1995-08-15",
"date_of_expiry": "2025-08-15",
"date_of_issue": "2020-08-15",
"sex": "F",
"address": "1234 ELM ST, SACRAMENTO, CA 95814",
"vehicle_class": "C"
}
}Frequently Asked Questions
How does StructOCR compare to AWS Textract or Google Vision?
Generic cloud OCR services like Textract or Vision return raw lines of text, leaving your team to parse, validate, and structure the data. StructOCR is a specialized model for identity documents. It bypasses the parsing step, delivering structured, labeled fields (e.g., `date_of_birth`, `document_number`) with built-in validation, reducing development time and improving accuracy.
Do you store the uploaded images?
No. We operate on a zero-retention policy. Images are processed entirely in-memory and are purged immediately after the API call completes. We do not persist PII on our systems.
How to handle blurry images?
Our API includes an automated image enhancement pipeline. Before OCR, it performs de-skewing, noise reduction, and sharpening to maximize accuracy even on sub-optimal, low-resolution, or blurry images captured from mobile devices.
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C# VIN (Vehicle Identification Number) OCR API
Tutorial: How to use the StructOCR C# Client to extract data from VIN (Vehicle Identification Number)s. Includes code samples and JSON schema.
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