Integration

Rekor CarCheck® is designed as a REST-based API, allowing you to interact with it using a variety of programming languages and operating systems. API calls can be made using different methods according to your development preferences. This flexibility ensures that you have control over how you interact with the API, enabling seamless integration into your applications.

Data Analysis and Retrieval

One of the key features of Rekor CarCheck® is its ability to analyze vehicle images from multiple sources. You can send vehicle image data from a local machine, server, or any web-based URL to Rekor CarCheck®. Upon receiving the image data, the API performs a comprehensive analysis, extracting information about the license plate and the corresponding vehicle. The results are returned in the form of JSON data, providing you with detailed insights about the recognized license plate and the associated vehicle.

API Endpoints

Rekor CarCheck® offers several endpoints that facilitate different functionalities within the API. Each endpoint serves a specific purpose, allowing you to perform various operations related to license plate recognition and vehicle identification.

Before getting started, you will want to make sure you have your secret key ready to go. Your secret key is available within the Web Dashboard. The following endpoints are available:

Send an image for Rekor to analyze and provide metadata back The image is sent as a file using a form data POST

POSThttps://api.openalpr.com/v3/recognize

Query parameters

Body

multipart/form-data

image*string (binary)

The image file that you wish to analyze

Response

200400401402403

OK

Headers

Body

application/json

processing_timeobject

img_widthinteger

Width of the uploaded image in pixels

img_heightinteger

Height of the input image in pixels

credit_costinteger

The number of API credits that were used to process this image

credits_monthly_usedinteger

The number of API credits used this month

credits_monthly_totalinteger

The maximum number of API credits available this month according to your plan

resultsarray of plate_details (object)

regions_of_interestarray of region_of_interest (object)

Describes the areas analyzed in the input image

vehiclesarray of vehicles (object)

Describes all vehicles found in the input image

epoch_timenumber

Epoch time that the image was processed in milliseconds

versioninteger

API format version

data_typeenum

Specifies the type of data in this response

alpr_resultsalpr_groupheartbeat

Request

JavaScriptCurl

const response = await fetch('https://api.openalpr.com/v3/recognize', {
    method: 'POST',
    headers: {
      "Content-Type": "multipart/form-data"
    },
    body: JSON.stringify({
      "image": "binary"
    }),
});
const data = await response.json();

Response

{
  "processing_time": {
    "total": 0,
    "plates": 0,
    "vehicles": 0
  },
  "results": [
    {
      "plate": "text",
      "processing_time_ms": 0,
      "confidence": 0,
      "region": "text",
      "region_confidence": 0,
      "coordinates": [
        {}
      ],
      "candidates": [
        {
          "plate": "text",
          "confidence": 0
        }
      ],
      "vehicle_region": {},
      "vehicle": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "regions_of_interest": [
    {}
  ],
  "vehicles": [
    {
      "details": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "epoch_time": 0,
  "data_type": "alpr_results"
}

Send an image for Rekor to analyze and provide metadata back The image is sent as a URL. The Rekor service will download the image and process it

POSThttps://api.openalpr.com/v3/recognize_url

Query parameters

Response

200400401402403

OK

Headers

Body

application/json

processing_timeobject

img_widthinteger

Width of the uploaded image in pixels

img_heightinteger

Height of the input image in pixels

credit_costinteger

The number of API credits that were used to process this image

credits_monthly_usedinteger

The number of API credits used this month

credits_monthly_totalinteger

The maximum number of API credits available this month according to your plan

resultsarray of plate_details (object)

regions_of_interestarray of region_of_interest (object)

Describes the areas analyzed in the input image

vehiclesarray of vehicles (object)

Describes all vehicles found in the input image

epoch_timenumber

Epoch time that the image was processed in milliseconds

versioninteger

API format version

data_typeenum

Specifies the type of data in this response

alpr_resultsalpr_groupheartbeat

Request

JavaScriptCurl

const response = await fetch('https://api.openalpr.com/v3/recognize_url', {
    method: 'POST',
    headers: {},
});
const data = await response.json();

Response

{
  "processing_time": {
    "total": 0,
    "plates": 0,
    "vehicles": 0
  },
  "results": [
    {
      "plate": "text",
      "processing_time_ms": 0,
      "confidence": 0,
      "region": "text",
      "region_confidence": 0,
      "coordinates": [
        {}
      ],
      "candidates": [
        {
          "plate": "text",
          "confidence": 0
        }
      ],
      "vehicle_region": {},
      "vehicle": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "regions_of_interest": [
    {}
  ],
  "vehicles": [
    {
      "details": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "epoch_time": 0,
  "data_type": "alpr_results"
}

Send an image for Rekor to analyze and provide metadata back The image is sent as base64 encoded bytes.

POSThttps://api.openalpr.com/v3/recognize_bytes

Query parameters

Body

application/json

The image file that you wish to analyze encoded in base64

string

Response

200400401402403

OK

Headers

Body

application/json

processing_timeobject

img_widthinteger

Width of the uploaded image in pixels

img_heightinteger

Height of the input image in pixels

credit_costinteger

The number of API credits that were used to process this image

credits_monthly_usedinteger

The number of API credits used this month

credits_monthly_totalinteger

The maximum number of API credits available this month according to your plan

resultsarray of plate_details (object)

regions_of_interestarray of region_of_interest (object)

Describes the areas analyzed in the input image

vehiclesarray of vehicles (object)

Describes all vehicles found in the input image

epoch_timenumber

Epoch time that the image was processed in milliseconds

versioninteger

API format version

data_typeenum

Specifies the type of data in this response

alpr_resultsalpr_groupheartbeat

Request

JavaScriptCurl

const response = await fetch('https://api.openalpr.com/v3/recognize_bytes', {
    method: 'POST',
    headers: {
      "Content-Type": "application/json"
    },
    body: JSON.stringify("text"),
});
const data = await response.json();

Response

{
  "processing_time": {
    "total": 0,
    "plates": 0,
    "vehicles": 0
  },
  "results": [
    {
      "plate": "text",
      "processing_time_ms": 0,
      "confidence": 0,
      "region": "text",
      "region_confidence": 0,
      "coordinates": [
        {}
      ],
      "candidates": [
        {
          "plate": "text",
          "confidence": 0
        }
      ],
      "vehicle_region": {},
      "vehicle": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "regions_of_interest": [
    {}
  ],
  "vehicles": [
    {
      "details": {
        "color": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "make_model": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "body_type": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "orientation": [
          {
            "name": "text",
            "confidence": 0
          }
        ],
        "year": [
          {
            "name": "text",
            "confidence": 0
          }
        ]
      }
    }
  ],
  "epoch_time": 0,
  "data_type": "alpr_results"
}

Get a list of available results for plate and vehicle recognition

GEThttps://api.openalpr.com/v3/config

Response

200

ok

Body

application/json

vehicle_labelsobject

countriesarray of object

Request

JavaScriptCurl

const response = await fetch('https://api.openalpr.com/v3/config', {
    method: 'GET',
    headers: {},
});
const data = await response.json();

Response

{
  "vehicle_labels": {
    "bodytype": [
      "sedan-compact"
    ],
    "orientation": [
      45
    ],
    "color": [
      "blue"
    ],
    "make": [
      "volvo"
    ],
    "makemodel": [
      "volvo_v40"
    ],
    "year": [
      "2000-2004"
    ]
  },
  "countries": [
    {
      "code": "us",
      "name": "North America"
    }
  ]
}