Medicare Health Insurance Card

This section describes how to build your custom OCR API to extract data from the Medicare Health Insurance card using the API Builder. A Medicare Health Insurance card is your proof of Medicare medical insurance.


You’ll need at least 20 Health insurance card images or pdfs to train your OCR.

Define Your Medical Health Insurance Card Use Case

Using the Medical Health Insurance Card below, we’re going to define the fields we want to extract from it.
Medical Health Insurance Card

  • Card owner: The full name card owner
  • ID Number: The unique identifier claim number
  • Sex: Gender of the card owner
  • Hospital effective date: Start date of the hospital coverage
  • Medical effective date: Start date of the medical coverage

That’s it for this example. Feel free to add any other relevant data that fits your requirement.

Deploy your API

Once you have defined the list of fields you want to extract from your Medicare Health Insurance card , head over to the platform and follow these steps:

  1. Click on the Create a new API button on the right.

  2. Next, fill in the basic information about the API you want to create as seen below.

Set up your API

  1. Click on the Next button. The following page allows you to define and add your data model.

Define Your Model

There are two ways to add fields to your data model.

  • Upload a JSON config file
  • Manually add data

Data Model

Upload a JSON Config

To add data fields using JSON config upload.

  1. Copy the following JSON into a file.
  "problem_type": {
    "classificator": { "features": [], "features_name": [] },
    "selector": {
      "features": [
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "cardholder",
          "public_name": "Cardholder",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "id_number",
          "public_name": "ID Number",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "sex",
          "public_name": "Sex",
          "semantics": "word"
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "hospital_effective_date",
          "public_name": "Hospital effective date",
          "semantics": "date"
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "medical_effective_date",
          "public_name": "Medical  effective date",
          "semantics": "date"
      "features_name": [
  1. Click on Upload a JSON config.
  2. The data model will be automatically filled.
  3. Click on Create API at the bottom of the screen.

Document Data Model filled

Manually Add Data

Using the interface, you can manually add each field for the data you are extracting. For this example, here are the different field configurations used:

  • Cardholder: type String The cardholder name never contains numeric characters, you can specify this by clicking the corresponding checkbox
  • ID Number: type String It's composed of both alpha and numeric characters so we don't put any specification for the claim number.
  • Sex: type String Like cardholder, there are no numeric characters for this one.
  • Hospital effective date: type Date
  • Medical effective date: type Date

Once you’re done setting up your data model, click the Create API button at the bottom of the screen.

Document Data Model filled

Train Your Medical Health Insurance Card OCR

You’re all set! Now it's time to train your Medical Health Insurance Card deep learning model in the Training section of our API.

Train your model

  1. Upload one file at a time or a zip bundle of many files.
  2. Click on the field input on the right, and the blue box on the left highlights all the corresponding field candidates in the document.
  3. Next, click on the validate arrow for all the field inputs.
  4. Once you have selected the proper box(es) for each of your fields as displayed on the right-hand side, click on the validate button located at the right-side bottom to send an annotation for the model you have created.
  5. Repeat this process until you have trained 20 documents to create a trained model.

To get more information about the training phase, please refer to the Getting Started tutorial.


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