Full-Field Brazil Vehicle Registration (CRLV / CRLV-e) OCR

The Challenge: Digital Screenshots and Moiré Patterns

Brazil has transitioned to a digital vehicle registration system (CRLV-e). However, the reality of user behavior means users frequently submit photos of their computer screens showing the digital PDF, or they print it on cheap A4 paper, fold it, and photograph it. Taking photos of screens introduces severe Moiré patterns (wavy distortion lines) that destroy standard OCR accuracy. Additionally, extracting the critical 'Renavam' code accurately is essential for any Brazilian automotive workflow. Learn more about our robust capabilities on our Vehicle Registration OCR product page.

Moiré Cancellation & Semantic Parsing

StructOCR is specifically engineered to handle degraded digital-to-physical captures. Our visual models actively filter out Moiré patterns and screen glare from laptop photos. Using advanced semantic Key-Value mapping, we isolate and extract universal vehicle fields while dynamically capturing Brazil-specific identifiers, like the Renavam, into our `localized.generic.unmapped_fields` object. Discover how this fits into broader Latin American compliance workflows in our Vehicle Registration Solutions guide.

Core Industry Applications

Integration & API Documentation

Easily integrate our full-field OCR API. For full technical details, visit our Developer API Documentation.

import requests
import base64

# 📸 Test for free with your own image upload: https://structocr.com/developers/vehicle-registration-ocr

with open("brazil-crlv-full-ocr.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

url = "https://api.structocr.com/v1/vehicle-registration"
headers = {
    "x-api-key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
payload = {
    "img": base64_image
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

if result.get('success'):
    data = result.get('data', {})
    std = data.get('standardized', {})
    print("✅ Standardized VIN:", std.get('vin'))
    
    # Brazil specific fields are captured in the generic fallback
    loc = data.get('localized', {})
    if 'generic' in loc and loc['generic'].get('unmapped_fields'):
        unmapped = loc['generic']['unmapped_fields']
        print("✅ Renavam:", unmapped.get('renavam'))
        print("✅ Chassi:", unmapped.get('chassi'))

{
  "success": true,
  "data": {
    "document": {
      "country_code": "BR",
      "document_type": "VEHICLE_REGISTRATION",
      "confidence_score": 0.98
    },
    "standardized": {
      "vin": "SAMPLEVIN12345678",
      "plate": {
        "number": "ABC-123",
        "formatted": "ABC 123"
      },
      "make_normalized": "Toyota",
      "year": 2020
    },
    "localized": {
      "generic": {
        "unmapped_fields": {
          "renavam": "12345678901",
          "chassi": "9BWZZZ37ZHT123456",
          "categoria": "Particular"
        }
      }
    }
  }
}