The Industry-Leading C# HIN (Hull Identification Number) OCR Library

Ditch inefficient regex. Use deep learning to extract standardized HIN data from complex boat images.

The Challenge of Parsing HINs

Extracting HINs from boat hulls presents significant technical hurdles. Challenges include high-glare reflections from fiberglass, curved hull deformation, blurring from years of saltwater corrosion, and inconsistent fonts used by different manufacturers (e.g., molded, engraved, or metal plates). Traditional OCR technologies often fail due to these complex background textures and varying perspective angles.

The StructOCR Solution for Marine Environments

StructOCR utilizes deep learning models specifically trained for marine environments. Our hin ocr api accurately processes skewed, glare-heavy, and non-standard fonts. The system automatically performs image enhancement, deskewing, and noise reduction before recognition, ensuring exceptional accuracy for data prefill even in harsh outdoor lighting conditions.

Common Use Cases

  • Marine Insurance & Claims: Accelerate policy quotes and claims processing by instantly capturing hull details.
  • Vessel Registration & Sales: Automate boat onboarding to ensure zero-error HIN records during storage, sales, and port registration.
  • Marine Service & Repair: Quickly identify vessels during service intakes to ensure ordered parts perfectly match the boat model.
Need more testing? Create a free account to get 200 free credits (equals 200 HIN scans).

Implementation: C# Request Example

Use this .NET example to quickly integrate HIN data extraction and return deeply parsed structured data.

Prerequisite: .NET Core 3.1+ / .NET 6+ / .NET 8+

CSHARP EXAMPLE
// Get 200 free credits:
// 👉 https://structocr.com/register

using System;
using System.IO;
using System.Net.Http;
using System.Text;
using System.Text.Json;
using System.Threading.Tasks;

public class HinOcrExample
{
    public static async Task Main(string[] args)
    {
        string apiKey = "YOUR_API_KEY"; 
        string imagePath = "path/to/boat_hin.jpg"; 

        try
        {
            // 1. Read the hull image file and convert it to Base64
            byte[] imageArray = await File.ReadAllBytesAsync(imagePath);
            string base64Image = Convert.ToBase64String(imageArray);

            // 2. Create the JSON payload
            var payload = new { img = base64Image };
            string jsonPayload = JsonSerializer.Serialize(payload);

            // 3. Configure the HTTP client
            using var httpClient = new HttpClient();
            httpClient.DefaultRequestHeaders.Add("x-api-key", apiKey);
            var content = new StringContent(jsonPayload, Encoding.UTF8, "application/json");

            // 4. Send the POST request to the StructOCR HIN endpoint
            HttpResponseMessage response = await httpClient.PostAsync("https://api.structocr.com/v1/hin", content);

            // 5. Handle the response
            if (response.IsSuccessStatusCode)
            {
                string responseBody = await response.Content.ReadAsStringAsync();
                using (JsonDocument document = JsonDocument.Parse(responseBody))
                {
                    JsonElement root = document.RootElement;
                    
                    // Read outer validation and confidence
                    Console.WriteLine($"HIN: {root.GetProperty("hin_number").GetString()}");
                    Console.WriteLine($"Is Valid: {root.GetProperty("is_valid").GetBoolean()}");
                    Console.WriteLine($"Confidence: {root.GetProperty("confidence").GetString()}");
                    
                    // Read deeply parsed data
                    JsonElement parsed = root.GetProperty("parsed");
                    Console.WriteLine($"Manufacturer: {parsed.GetProperty("manufacturer_code").GetString()}");
                    Console.WriteLine($"Year: {parsed.GetProperty("model_year").GetString()}");
                }
            }
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Exception: {ex.Message}");
        }
    }
}

Technical Specs

  • •Latency: < 4s (Average)
  • •Availability: Served from Cloudflare's global edge network
  • •Security: AES-256 Encryption & SOC2 Compliant
  • •Input: JPG, PNG, WebP (Max 4.5MB)
  • •Output: Deeply parsed structured JSON

Key Features

  • •Smart Deskew: Geometrically corrects text distortion caused by curved boat hulls.
  • •Intelligent Parsing: Automatically extracts country codes, manufacturers, production dates, and model years.
  • •Specialized Models: Highly optimized for outdoor, low-light environments, and metal/fiberglass surfaces.

Sample JSON Response

The API not only returns the raw HIN string but also automatically validates it and deeply parses core fields like the production month and manufacturer.

{
  "hin_number": "US-YAMC0323F313",
  "is_valid": true,
  "validation_error": null,
  "confidence": "High",
  "parsed": {
    "country_code": "US",
    "manufacturer_code": "YAM",
    "serial_number": "C0323",
    "production_month": "June",
    "production_year_short": "3",
    "model_year": "2013"
  }
}

Frequently Asked Questions

What types of vessels are supported by the API?

We support almost all watercraft, including yachts, sailboats, jet skis, speedboats, and workboats, covering various international and manufacturer-specific HIN encoding standards.

Can it recognize HINs that are blurred due to saltwater corrosion or wear?

Yes. Our models are specifically trained on scratches, rust, and wear common in marine environments. Furthermore, our built-in checksum logic can help reconstruct or validate the overall code even if partial characters are degraded.

How is image data and privacy security handled?

We hold SOC 2 Type II, and client images are kept for at most 24 hours for quality spot-checks, then deleted.

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