Vehicle registration
automation

What we do

In order to automate document tasks all around, our software has been trained on vehicle documents. We can extract all relevant information from your documents in real time. The entire process is super easy and quick to integrate.
Thanks to its Deep-OCR, natif.ai Vehicle Registration Automation recognizes all the important information in the vehicle documents.
The software delivers real-time results, reduces manual effort, requires no setup costs and produces immediate savings. Plus, the entire process is super easy and quick to integrate.

Plug & Play

Thanks to Rest API, our AI models are quickly integrated into existing systems.

Accuracy

Our AI models have been trained with hundreds of thousands of documents and extract all important information with high precision.

Speed

Thanks to high-performance GPUs, our AI processes documents in real time.

Swarm intelligence

Our AI benefits from feedback from all our customers, so problems don’t have to be solved by each customer anew.

Active learning

Automated processes are constantly improving the recognition rates of our technology.

Robust reading

Our Deep OCR reads even low-quality documents and can be specially adapted to difficult use cases.

Data protection

We are 100 % GDPR- and Schrems II-compliant. We process the data on our servers and dispense with service providers.

AI Boost

Our AI models directly enable your software to automate documents.

Vehicle registration no.

L-A-0-332/13-20292

Name or company name

Melzer

Date

01.02.2012

Trade name

Fiat Ducato

Pollutant class

Diesel

{
  "schema_version": 1,
  "document_type": "vehicle_registration",
  "fahrzeugschein_nr": {
    "validation_problem": false,
    "note": "",
    "confidence": 0.99985,
    "bbox_refs": [
      {
        "page_num": 1,
        "bbox_id": 34
      },
      {
        "page_num": 1,
        "bbox_id": 35
      }
    ],
    "value": "L-A-0-332/13-20292"
  },
  "a_amtliches_kennzeichen": {
    "validation_problem": false,
    "note": "",
    "confidence": 0.999925,
    "bbox_refs": [
      {
        "page_num": 1,
        "bbox_id": 36
      },
      {
        "page_num": 1,
        "bbox_id": 37
      },
      {
        "page_num": 1,
        "bbox_id": 30
      },
      {
        "page_num": 1,
        "bbox_id": 31
      }
    ],
    "value": "LAH6661"
    "c_1_1_name_des_halters": {
      "validation_problem": false,
      "note": "",
      "confidence": 0.999925,
      "bbox_refs": [
        {
          "page_num": 1,
          "bbox_id": 36
        },
        {
          "page_num": 1,
          "bbox_id": 37
        },
        {
          "page_num": 1,
          "bbox_id": 30
        },
        {
          "page_num": 1,
          "bbox_id": 31
        }
      ],
      "value": "Melzer"    
....
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How we do it

Our pre-trained model for the automation of vehicle documents extracts all relevant information from the documents. Whether position data, various tax rates or discounts, our technology recognizes and extracts them for downstream software systems so that the entire process can be fully automated.

Rest API

C#
Python
PHP
NodeJS
Java
using Newtonsoft.Json.Linq;

using (HttpClient httpClient = new HttpClient())
{
    string baseURL = "https://api.natif.ai";
    string workflow = "invoice_extraction";
    string apiKey = "<API_KEY>";    // TODO: Insert or load your API-key secret here
    string filePath = "<FILE_PATH>";    // TODO: Insert or load your file path here

    using (HttpRequestMessage request = new HttpRequestMessage(new HttpMethod("POST"), baseURL + "/processing/" + workflow + "?include=extractions"))
    {
        request.Headers.TryAddWithoutValidation("accept", "application/json");
        request.Headers.TryAddWithoutValidation("Authorization", "ApiKey " + apiKey);

        MultipartFormDataContent multipartContent = new MultipartFormDataContent();
        ByteArrayContent file1 = new ByteArrayContent(File.ReadAllBytes(filePath));
        multipartContent.Add(file1, "file", Path.GetFileName(filePath));
        multipartContent.Add(new StringContent("{\"language\": \"de\"}"), "parameters");
        request.Content = multipartContent;

        HttpResponseMessage response = await httpClient.SendAsync(request);

        string responseString = await response.Content.ReadAsStringAsync();
        JObject responseJson = JObject.Parse(responseString);
        if (responseJson["extractions"]["vendor"] != null
            && responseJson["extractions"]["vendor"]["name"] != null)
        {
            JToken vendorName = responseJson["extractions"]["vendor"]["name"];
            Console.WriteLine("Vendor name: " + vendorName["value"].ToString());
            Console.WriteLine("Vendor name confidence: " + vendorName["confidence"].ToString());
        }
        else
        {
            Console.WriteLine("Vendor name could not be extracted.");
        }
    }
}
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Rest API

Can be integrated into existing processes in just a few minutes, thanks to support for common programming languages such as C#, PHP, Python, Java or NodeJS. Our documentation easily guides you through the integration processes. We support both synchronous and asynchronous data transfer. 
In the first step, documents can be uploaded via the API. Our software supports a whole range of file formats. These are currently the image formats JPEG, PNG, BMMP, GIF, TIFF and the document format PDF. 

Einfach Integration dank Make-Plugin

Easy integration thanks to Make-Plugin

Intuitive use of our technology and easy integration into existing processes such as in the DocuWare context thanks to our Make plugin.

Data extraction

We provide the key information as a structured JSON format via the API. For each data point, it also specifies the exact position on the document, the value and the confidence level. In addition to the JSON file, we provide a PDF/A file with the text level.
{
  "processing_id": "bfb819fb-b193-42fa-ad19-8ad33aa8831b",
  "available_results": [
    "pdf",
    "ocr",
    "page-images",
    "hocr",
    "language",
    "thumbnail"
  ],
  "pdf": "/processing/results/bfb819fb-b193-42fa-ad19-8ad33aa8831b/pdf",
  "ocr": {
    "pages": [
      {
        "width": 2380,
        "height": 3368,
        "bboxes": [
          {
            "id": 1,
            "x1": 82,
            "y1": 333,
            "x2": 102,
            "y2": 351,
            "text": "a",
            "text_entropy": 0.58203125
          },
          {
            "id": 2,
            "x1": 132,
            "y1": 1508,
            "x2": 551,
            "y2": 1558,
            "text": "Staatsangehörigkeit:",
            "text_entropy": 0.141357421875
          },
          {
            "id": 3,
            "x1": 140,
            "y1": 1975,
            "x2": 310,
            "y2": 2014,
            "text": "Aktuelle",
            "text_entropy": 0.11651611328125
          },
          {
            "id": 4,
            "x1": 317,
            "y1": 1974,
            "x2": 625,
            "y2": 2019,
            "text": "Jahresbeiträge:",
            "text_entropy": 0.0947265625
          },
          {
            "id": 5,
            "x1": 138,
            "y1": 1081,
            "x2": 440,
            "y2": 1119,
            "text": "Geburtsdatum:",
            "text_entropy": 0.138671875
          },
          {
            "id": 6,
            "x1": 132,
            "y1": 2039,
            "x2": 477,
            "y2": 2075,
            "text": "Familienmitgliedschaft",
            "text_entropy": 0.14794921875
          },
          {
            "id": 7,
            "x1": 483,
            "y1": 2040,
            "x2": 534,
            "y2": 2074,
            "text": "gilt",
            "text_entropy": 0.07037353515625
          },
          {
            "id": 8,
            "x1": 536,
            "y1": 2040,
            "x2": 586,
            "y2": 2074,
            "text": "für",
            "text_entropy": 0.06671142578125
          },
          {
            "id": 9,
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            "x2": 639,
            "y2": 2070,
            "text": "1-2",
            "text_entropy": 0.07904052734375
          },
          {
            "id": 10,
            "x1": 645,
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            "x2": 820,
            "y2": 2071,
            "text": "Erwachsene",
            "text_entropy": 0.1488037109375
          },
          {
            "id": 11,
            "x1": 822,
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            "x2": 891,
            "y2": 2071,
            "text": "incl.",
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          },
          {
            "id": 12,
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            "x2": 982,
            "y2": 2071,
            "text": "deren",
            "text_entropy": 0.06317138671875
          },
          {
            "id": 13,
            "x1": 984,
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            "x2": 1081,
            "y2": 2071,
            "text": "Kinder",
            "text_entropy": 0.0614013671875
          },
          {
            "id": 14,
            "x1": 1087,
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            "x2": 1132,
            "y2": 2070,
            "text": "bis",
            "text_entropy": 0.0265045166015625
          },
          {
            "id": 15,
            "x1": 1134,
            "y1": 2040,
            "x2": 1334,
            "y2": 2075,
            "text": "einschließlich",
            "text_entropy": 0.1434326171875
          },
          {
            "id": 16,
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          },
          {
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          },
          {
            "id": 18,
            "x1": 1465,
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          },
          {
            "id": 19,
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            "x2": 1744,
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            "text": "welche",
            "text_entropy": 0.12548828125
          },
          {
            "id": 20,
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            "x2": 1801,
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          {
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          {
            "id": 22,
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            "text": "einem",
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          },
          {
            "id": 23,
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            "text": "Haushalt",
            "text_entropy": 0.100341796875
          },
          {
            "id": 24,
            "x1": 2076,
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            "x2": 2173,
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            "text": "leben.",
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          },
          {
            "id": 25,
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            "text": "Straße,",
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          },
          {
            "id": 26,
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            "text": "HsNr.:",
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          },
          {
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          {
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          },
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        ],
        "fulltext": "Aufnahmeantrag\n|\nfür Erwachsene u. Jugendliche ab 16\na\nincl. Sepamandat u. Datenschutzerklärung\nHiermit beantrage ich die Mitgliedschaft im Sportverein NatifSports.\n(Zudem beantrage ich als aktive/r Fußballspieler/in ebenfalls die Mitgliedschaft im FC 92 Natif, solange der Spielbetrieb dies\nerfordert.)\nn\n01.01.2023\nMaximilian\nMustermann\n13.06.1990\nZum Vereinsheim\n33445\n031 65432\nEintrittsdatum:\nVorname:\nName:\nAm\n[X.m\nGeburtsdatum:\nGeschlecht:\n17\nd\nW\nStraße, HsNr.:\nMusterstadt\nPLZ, Ort:\n749. 151 687899\nFestnetz:\nMobil:\nStaatsangehörigkeit: X\ntsch\n(andere)\nDeutsch\nGymnastik / Aerobic / Turnen u.ä.\nLAktives Mitglied in der Sparte\nFußball\nBoxen\nals Fördermitglied mit\nvollem Beitrag\n% Beitrag\nFamilienmitgliedschaft über folgenden Hauptzahler:\nAktuelle Jahresbeiträge:\nFamilienmitgliedschaft gilt für 1-2 Erwachsene incl. deren Kinder bis einschließlich dem 18. Lebensjahr, welche alle in einem Haushalt leben.\nErwachsene 50,-- €\nKinder-u. Jugendliche 25,-- €\nFamilien 90,-- €\nIch habe Kenntnis von der Satzung genommen und erkenne diese an. Die Satzung liegt jederzeit im Sport-\nheim aus und ist im Internet unter natif.ai zu finden.\nMusterstadt, , 30.12.2022\nOrt, Datum\nM. Mustermann\nUnterschrift des neuen Mitglieds\nUnterschrift Erziehungsberechtige/r\nUnterschrift Erziehungsberechtige/r\nBei Minderjährigen sind generell die Unterschriften aller Erziehungsberechtigter erforderlich.\nMit der Unterschrift erkläre ich mich/wir uns bereit, die Beitragszahlung zu übernehmen. Diese Schuld-\nübernahme ist bis zur Volljährigkeit des Kindes begrenzt. Ich stimme der Speicherung, Verarbeitung und\nNutzung meiner personenbezogenen Daten (siehe Datenschutzerklärung) zu, soweit es für Vereins-/Ver-\nbandszwecke erforderlich ist.\nEin Vereinsaustritt hat immer mindestens einen Monat vor Jahresende schriftlich zu erfolgen und ist je-\nweils nur zum Jahresende gültig. Die Datenschutzerklärung ist zwingend notwendig, bei Verweigerung\nder selbigen, kann die Mitgliedschaft nicht erfolgen."
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