Carte Vitale - FRANCE OCR

Mindee’s Carte Vitale - FRANCE API uses deep learning to automatically, accurately, and instantaneously parse your card details. In under a second, the API extracts a set of data from your PDFs or photos of Carte Vitale, including:

Given Name(s)
Surname
Social Security Number
Issuance Date

The Carte Vitale - France API supports the new and old format for all people in France. The cards of other nationalities and states are not supported with this model.

Set up the API

Create an API key

To begin using the Mindee V1 OCR API, your first step is to create your V1 API key.

You'll need a french carte vitale. You can use one of the sample documents provided below.
Access your API by clicking on the Carte Vitale - FRANCE card in the Document Catalog
From the left navigation, go to documentation > API Reference, you'll find sample code in popular languages and command line.

from mindee import Client, product, AsyncPredictResponse

# Init a new client
mindee_client = Client(api_key="my-api-key-here")

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Load a file from disk and enqueue it.
result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
    product.fr.HealthCardV1,
    input_doc,
)

# Print a brief summary of the parsed data
print(result.document)

const mindee = require("mindee");
// for TS or modules:
// import * as mindee from "mindee";

// Init a new client
const mindeeClient = new mindee.Client({ apiKey: "my-api-key-here" });

// Load a file from disk
const inputSource = mindeeClient.docFromPath("/path/to/the/file.ext");

// Parse the file
const apiResponse = mindeeClient.enqueueAndParse(
  mindee.product.fr.HealthCardV1,
  inputSource
);

// Handle the response Promise
apiResponse.then((resp) => {
  // print a string summary
  console.log(resp.document.toString());
});

using Mindee;
using Mindee.Input;
using Mindee.Product.Fr.HealthCard;

string apiKey = "my-api-key-here";
string filePath = "/path/to/the/file.ext";

// Construct a new client
MindeeClient mindeeClient = new MindeeClient(apiKey);

// Load an input source as a path string
// Other input types can be used, as mentioned in the docs
var inputSource = new LocalInputSource(filePath);

// Call the product asynchronously with auto-polling
var response = await mindeeClient
    .EnqueueAndParseAsync<HealthCardV1>(inputSource);

// Print a summary of all the predictions
System.Console.WriteLine(response.Document.ToString());

// Print only the document-level predictions
// System.Console.WriteLine(response.Document.Inference.Prediction.ToString());

#
# Install the Ruby client library by running:
# gem install mindee
#

require 'mindee'

# Init a new client
mindee_client = Mindee::Client.new(api_key: 'my-api-key')

# Load a file from disk
input_source = mindee_client.source_from_path('/path/to/the/file.ext')

# Parse the file
result = mindee_client.parse(
  input_source,
  Mindee::Product::FR::HealthCard::HealthCardV1
)

# Print a full summary of the parsed data in RST format
puts result.document

# Print the document-level parsed data
# puts result.document.inference.prediction

import com.mindee.MindeeClient;
import com.mindee.input.LocalInputSource;
import com.mindee.parsing.common.AsyncPredictResponse;
import com.mindee.product.fr.healthcard.HealthCardV1;
import java.io.File;
import java.io.IOException;

public class SimpleMindeeClient {

  public static void main(String[] args) throws IOException, InterruptedException {
    String apiKey = "my-api-key-here";
    String filePath = "/path/to/the/file.ext";

    // Init a new client
    MindeeClient mindeeClient = new MindeeClient(apiKey);

    // Load a file from disk
    LocalInputSource inputSource = new LocalInputSource(new File(filePath));

    // Parse the file asynchronously
    AsyncPredictResponse<HealthCardV1> response = mindeeClient.enqueueAndParse(
        HealthCardV1.class,
        inputSource
    );

    // Print a summary of the response
    System.out.println(response.toString());

    // Print a summary of the predictions
//  System.out.println(response.getDocumentObj().toString());

    // Print the document-level predictions
//    System.out.println(response.getDocumentObj().getInference().getPrediction().toString());

    // Print the page-level predictions
//    response.getDocumentObj().getInference().getPages().forEach(
//        page -> System.out.println(page.toString())
//    );
  }

}

API_KEY='my-api-key-here'
ACCOUNT='mindee'
ENDPOINT='french_healthcard'
VERSION='1'
FILE_PATH='/path/to/your/file.png'

# Maximum amount of retries to get the result of a queue
MAX_RETRIES=10

# Delay between requests
DELAY=6

# Enqueue the document for async parsing
QUEUE_RESULT=$(curl -sS --request POST \
  -H "Authorization: Token $API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "document=@$FILE_PATH" \
  "https://api.mindee.net/v1/products/$ACCOUNT/$ENDPOINT/v$VERSION/predict_async")

# Status code sent back from the server
STATUS_CODE=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']status_code[\"|']:[\s][\"|']*[a-zA-Z0-9-]*" | rev | cut --complement -f2- -d" " | rev)

# Check that the document was properly queued
if [ -z "$STATUS_CODE" ] || [ "$STATUS_CODE" -gt 399 ] || [ "$STATUS_CODE" -lt 200 ]
then
  if [ -z "$STATUS_CODE" ]
  then
    echo "Request couldn't be processed."
    exit 1
  fi
  echo "Error $STATUS_CODE was returned by API during enqueuing. "

  # Print the additional details, if there are any:
  ERROR=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']error[\"|']:[\s]\{[^\}]*" | rev | cut --complement -f2- -d"{" | rev)
  if [ -z "$ERROR" ]
  then
    exit 1
  fi

  # Details on the potential error:
  ERROR_CODE=$(echo "$ERROR" | grep -oP "[\"|']code[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  MESSAGE=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']message[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  DETAILS=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']details[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  echo "This was the given explanation:"
  echo "-------------------------"
  echo "Error Code: $ERROR_CODE"
  echo "Message: $MESSAGE"
  echo "Details: $DETAILS"
  echo "-------------------------"
  exit 1
else

  echo "File sent, starting to retrieve from server..."

  # Get the document's queue ID
  QUEUE_ID=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']id[\"|']:[\s][\"|'][a-zA-Z0-9-]*" | rev | cut --complement -f2- -d"\"" | rev)

  # Amount of attempts to retrieve the parsed document were made
  TIMES_TRIED=1

  # Try to fetch the file until we get it, or until we hit the maximum amount of retries
  while [ "$TIMES_TRIED" -lt "$MAX_RETRIES" ]
  do
    # Wait for a bit at each step
    sleep $DELAY

    # Note: we use -L here because the location of the file might be behind a redirection
    PARSED_RESULT=$(curl -sS -L \
      -H "Authorization: Token $API_KEY" \
      "https://api.mindee.net/v1/products/$ACCOUNT/$ENDPOINT/v$VERSION/documents/queue/$QUEUE_ID")

    # Isolating the job (queue) & the status to monitor the document
    JOB=$(echo "$PARSED_RESULT" | grep -ioP "[\"|']job[\"|']:[\s]\{[^\}]*" | rev | cut --complement -f2- -d"{" | rev)
    QUEUE_STATUS=$(echo "$JOB" | grep -ioP "[\"|']status[\"|']:[\s][\"|'][a-zA-Z0-9-]*" | rev | cut --complement -f2- -d"\"" | rev)
    if [ "$QUEUE_STATUS" = "completed" ]
    then
      # Print the result
      echo "$PARSED_RESULT"

      # Optional: isolate the document:
      # DOCUMENT=$(echo "$PARSED_RESULT" | grep -ioP "[\"|']document[\"|']:[\s].*([\"|']job[\"|'])" | rev | cut -f2- -d"," | rev)
      # echo "{$DOCUMENT}"

      # Remark: on compatible shells, fields can also be extracted through the use of tools like jq:
      # DOCUMENT=$(echo "$PARSED_RESULT" | jq '.["document"]')
      exit 0
    fi
    TIMES_TRIED=$((TIMES_TRIED+1))
  done
fi

echo "Operation aborted, document not retrieved after $TIMES_TRIED tries"
exit 1

<?php

use Mindee\Client;
use Mindee\Product\Fr\HealthCard\HealthCardV1;

// Init a new client
$mindeeClient = new Client("my-api-key-here");

// Load a file from disk
$inputSource = $mindeeClient->sourceFromPath("/path/to/the/file.ext");

// Parse the file asynchronously
$apiResponse = $mindeeClient->enqueueAndParse(HealthCardV1::class, $inputSource);

echo $apiResponse->document;

Replace my-api-key-here with your new API key, or use the select an API key feature and it will be filled automatically.
Copy and paste the sample code of your desired choice in your application, code environment, terminal etc.
Replace /path/to/the/file.ext with the path to your input document.

Remember to replace with your V1 API key.

Run your code. You will receive a JSON response with the document details.

API Response

Here is the full JSON response you get when you call the API:

{
  "api_request": {
    "error": {},
    "resources": [
      "document",
      "job"
    ],
    "status": "success",
    "status_code": 200,
    "url": "https://api.mindee.net/v1/products/mindee/french_healthcard/v1/documents/1ec5aff1-ef20-4744-bf7e-fe03464192f3"
  },
  "document": {
    "id": "1ec5aff1-ef20-4744-bf7e-fe03464192f3",
    "inference": {
      "extras": {},
      "finished_at": "2024-10-28T10:07:18.627000",
      "is_rotation_applied": true,
      "pages": [
        {
          "extras": {},
          "id": 0,
          "orientation": {
            "value": 0
          },
          "prediction": {}
        }
      ],
      "prediction": { ... },
      "processing_time": 2.084,
      "product": {
        "features": [
          "given_names",
          "surname",
          "social_security",
          "issuance_date"
        ],
        "name": "mindee/french_healthcard",
        "type": "standard",
        "version": "1.0"
      },
      "started_at": "2024-10-28T10:07:16.391000"
    },
    "n_pages": 1,
    "name": "CarteVitale2 (1).jpg"
  },
  "job": {
    "available_at": "2024-10-28T10:07:18.636000",
    "error": {},
    "id": "097af697-b4dc-4a4a-9374-9e64fb009b01",
    "issued_at": "2024-10-28T10:07:16.391000",
    "status": "completed"
  }
}

You can find the prediction within the prediction key found in document > inference > prediction for document-level predictions: it contains the different fields extracted at the document level, meaning that for multi-pages PDFs, we reconstruct a single object using all the pages.

Each predicted field may contain one or several values:

a polygon highlighting the information location
a page_id where the information was found (document level only)

{
  "api_request": { ... },
  "document": {
       ...
      "prediction": {
        "given_names": [
          {
            "page_id": 0,
            "polygon": [
              [
                0.282,
                0.671
              ],
              [
                0.441,
                0.671
              ],
              [
                0.441,
                0.709
              ],
              [
                0.282,
                0.709
              ]
            ],
            "value": "NATHALIE"
          }
        ],
        "issuance_date": {
          "page_id": 0,
          "polygon": [
            [
              0.443,
              0.438
            ],
            [
              0.605,
              0.438
            ],
            [
              0.605,
              0.475
            ],
            [
              0.443,
              0.475
            ]
          ],
          "value": "2007-01-01"
        },
        "social_security": {
          "page_id": 0,
          "polygon": [
            [
              0.282,
              0.838
            ],
            [
              0.616,
              0.838
            ],
            [
              0.616,
              0.873
            ],
            [
              0.282,
              0.873
            ]
          ],
          "value": "2 69 05 49 588 157 80"
        },
        "surname": {
          "page_id": 0,
          "polygon": [
            [
              0.284,
              0.727
            ],
            [
              0.422,
              0.727
            ],
            [
              0.422,
              0.766
            ],
            [
              0.284,
              0.766
            ]
          ],
          "value": "DURAND"
        }
      },
...

  }
}

Detailed Field Information

Depending on the field type specified, additional attributes can be extracted from the Carte Vitale -FRANCE card object. Using the above example the following are the basic fields that can be extracted.

Given Name(s)

given_names: The given names of the card holder.

{
  "given_names": [
    {
      "page_id": 0,
      "polygon": [[0.282,0.671],[0.441,0.671],[0.441,0.709],[0.282,0.709]],
      "value": "NATHALIE"
    }
  ]
}

Surname

surname: The surname of the card holder.

{
  "surname": {
    "page_id": 0,
    "polygon": [[0.284,0.727],[0.422,0.727],[0.422,0.766],[0.284,0.766]],
    "value": "DURAND"
  }
}

social_security: The social security number of the card holder.

{
  "social_security": {
    "page_id": 0,
    "polygon": [[0.282,0.838],[0.616,0.838],[0.616,0.873],[0.282,0.873]],
    "value": "2 69 05 49 588 157 80"
  }
}

Issuance Date

issuance_date: The date when the carte vitale was issued.

{
  "issuance_date": {
    "page_id": 0,
    "polygon": [[0.443,0.438],[0.605,0.438],[0.605,
        0.475
      ],
      [
        0.443,
        0.475
      ]
    ],
    "value": "2007-01-01"
  }
}

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Set up the API

API Response

Detailed Field Information

Given Name(s)

Surname

Social Security Number

Issuance Date