We recently launched our Vehicle Identification Number (VIN) Recognition Model 3.0 (VIN OCR) which can accurately and swiftly extract VIN from various images. This model is equipped with advanced Optical Character Recognition (OCR) technology and a decoder. It can not only accurately read the VIN but also get basic vehicle information such as make, model, year based on the VIN.
Whether it is vehicle registration certificate, insurance policy, invoice, document or handwritten images, this VIN recognition model can accurately extract the VIN. It is widely used in used car transactions, insurance claims, fleet management and other scenarios to improve work efficiency and achieve automation.

The VIN recognition API can be highly customized and integrated into your website, app or system. With a simple API call, your users, customers or employees can get accurate VIN information and vehicle details in seconds.

This VIN OCR model is trained using industry-leading OCR technology and a large amount of labeled data, with a recognition accuracy of over 99%. And the model will continue to learn and upgrade, with recognition capabilities becoming more intelligent and powerful.
Use our VIN recognition API to get vehicle information faster, more accurate and smarter!

Recognition is not limited to the following images

The following errors may occur when locating and capturing the VIN in the image.

The following error may occur when the system decodes the VIN.

#If you are sending a file, see this Python example
import requests
url = "https://vin-recognition.p.rapidapi.com/v2"
#payload = {'inputurl': 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/......'}
payload = {'inputurl': 'http://www.sample.com/smaple_vin.jpg',"dchannel": "d0"}
headers = { "X-RapidAPI-Key": "Your key", "X-RapidAPI-Host": "vin-recognition.p.rapidapi.com" }
response = requests.post(url, data=payload, headers=headers)
print(response.json())

#If you are sending a base64 image or url, look at this example
import requests
url = "https://vin-recognition.p.rapidapi.com/v2"
files = {"inputimage": open('/path/sample.jpg', 'rb')}
payload = {"dchannel": "d0"}
headers = {"X-RapidAPI-Key": "Your key", "X-RapidAPI-Host": "vin-recognition.p.rapidapi.com"}
response = requests.post(url, data=payload, files=files, headers=headers)
print(response.json())

Success

 {
    "service": "vinocr",
    "version": "3.0",
    "date": "1/12/2022 5:00:36 AM",
    "status": "SUCCESS",
    "vin_captured": "19UYA42601A019296",
    "vindecode": {
        "status": "SUCCESS",
        "make": "Acura",
        "model": "CL",
        "year": 2001
    },
    "left": 93.0,
    "top": 217.0,
    "width": 328.0,
    "height": 37.0
}

No decode

{
"service": "vinocr",
"version": "3.0",
"date": "1/12/2022 5:00:36 AM",
"status": "SUCCESS",
"vincaptured": "1FAHP25NX8W134012", "vindecode": { "status": "FAILED", "messagekey": 2,
"message": "Valid VIN number. However, no data available for it at this moment."
},
"left": 235.0,
"top": 182.0,
"width": 121.0,
"height": 10.0
}

No VIN

{
"service": "vinocr",
"version": "2.0",
"date": "1/12/2022 5:00:36 AM",
"status": "FAILED",
"message_key": 26,
"message": "Recognition failed."
}