Carte Vitale - FRANCE OCR
Automatically extract data from french “Carte Vitale”.
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
Before making any API calls, you need to have created your 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, AsyncPredictResponse, product
# Init a new client
mindee_client = Client(api_key="my-api-key-here")
# Add the corresponding endpoint (document). Set the account_name to "mindee" if you are using OTS.
my_endpoint = mindee_client.create_endpoint(
account_name="mindee",
endpoint_name="french_healthcard",
version="1"
)
# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
# Parse the file.
# The endpoint must be specified since it cannot be determined from the class.
result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
product.GeneratedV1,
input_doc,
endpoint=my_endpoint
)
# Print a brief summary of the parsed data
print(result.document)
# # Iterate over all the fields in the document
# for field_name, field_values in result.document.inference.prediction.fields.items():
# print(field_name, "=", field_values)
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");
// Create a custom endpoint for your product
const customEndpoint = mindeeClient.createEndpoint(
"french_healthcard",
"mindee",
"1" // Defaults to "1"
);
// Parse the file asynchronously.
const asyncApiResponse = mindeeClient.enqueueAndParse(
mindee.product.GeneratedV1,
inputSource,
{ endpoint: customEndpoint }
);
// Handle the response Promise
asyncApiResponse.then((resp) => {
// print a string summary
console.log(resp.document.toString());
});
using Mindee;
using Mindee.Input;
using Mindee.Http;
using Mindee.Product.Generated;
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);
// Set the endpoint configuration
CustomEndpoint endpoint = new CustomEndpoint(
endpointName: "french_healthcard",
accountName: "mindee",
version: "1"
);
// Call the product asynchronously with auto-polling
var response = await mindeeClient
.EnqueueAndParseAsync<GeneratedV1>(inputSource, endpoint);
// 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());
require 'mindee'
# Init a new client
mindee_client = Mindee::Client.new(api_key: 'my-api-key-here')
# Load a file from disk
input_source = mindee_client.source_from_path('/path/to/the/file.ext')
# Initialize a custom endpoint for this product
custom_endpoint = mindee_client.create_endpoint(
account_name: 'mindee',
endpoint_name: 'french_healthcard',
version: '1'
)
# Parse the file
result = mindee_client.enqueue_and_parse(
input_source,
Mindee::Product::Generated::GeneratedV1,
endpoint: custom_endpoint
)
# Print a full summary of the parsed data in RST format
puts result.document
import com.mindee.MindeeClient;
import com.mindee.input.LocalInputSource;
import com.mindee.parsing.common.AsyncPredictResponse;
import com.mindee.product.generated.GeneratedV1;
import com.mindee.http.Endpoint;
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));
// Configure the endpoint
Endpoint endpoint = new Endpoint(
"french_healthcard",
"mindee",
"1"
);
// Parse the file asynchronously
AsyncPredictResponse<GeneratedV1> response = mindeeClient.enqueueAndParse(
GeneratedV1.class,
endpoint,
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\Generated\GeneratedV1;
use Mindee\Input\PredictMethodOptions;
// Init a new client
$mindeeClient = new Client("my-api-key-here");
// Load a file from disk
$inputSource = $mindeeClient->sourceFromPath("/path/to/the/file.ext");
// Create a custom endpoint
$customEndpoint = $mindeeClient->createEndpoint(
"french_healthcard",
"mindee",
"1"
);
// Add the custom endpoint to the prediction options.
$predictOptions = new PredictMethodOptions();
$predictOptions->setEndpoint($customEndpoint);
// Parse the file
$apiResponse = $mindeeClient->enqueueAndParse(GeneratedV1::class, $inputSource, $predictOptions);
echo strval($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 or terminal.
- Replace
/path/to/my/file
with the path to your document.
Always remember to replace your 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"
}
},
...
}
}
Extracted data
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 Number
- 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"
}
}
Questions?
Updated about 1 month ago