DS 2019 Form

This section describes how to build your custom OCR API to extract data from a DS-2019 document using the API Builder. A DS-2019 Form is a document that allows you to apply for the J-1 Visa as an intern or trainee in the USA.


You’ll need at least 20 DS 2019 form images or pdfs to train your OCR.

Define Your DS 2019 Use Case

Using the DS 2019 below, we’re going to define the fields we want to extract from it.
DS 2019

  • First Name: The first name of the DS 2019 holder
  • Family Name: The last name of the DS 2019 holder
  • Gender: The DS 2019 holder gender
  • Visa type: The visa type linked to the DS 2019 form
  • End of cover period: The ending date of the DS 2019 cover period

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 DS 2019 form, 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.

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": "first_name",
          "public_name": "First name",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "family_name",
          "public_name": "Family name",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "gender",
          "public_name": "Gender",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "visa_type",
          "public_name": "Visa Type",
          "semantics": "word"
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "end_of_cover_period",
          "public_name": "End of cover period",
          "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:

  • First name: type String that never contains numeric characters.
  • Family name: type String that never contains numeric characters.
  • Gender: type String that never contains numeric characters.
  • Visa Type: type String without specifications.
  • End of cover period: type Date in US format.

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 DS 2019 Form OCR

You’re all set! Now it's time to train your DS 2019 Form 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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