This section describes how to build your custom OCR API to extract data from W9 Forms using the API Builder. A W9 Form is used in the United States income tax system by a third party who must file an information return with the Internal Revenue Service.

Prerequisites

You’ll need at least 20 W9 images or pdfs to train your OCR.

Define your W9 Form Use Case

Using the W9 Form below, we’re going to define the fields we want to extract from it.
W9 FormW9 Form

  • Name: The taxpayer's name.
  • Address: The taxpayer's mailing address (number, street, and apt)
  • City: The taxpayer's city.
  • State: The taxpayer's state.
  • Zip Code: The taxpayer's zip code.
  • Date: The date the W9 was filled.
  • Employer ID: The employer identification number.

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 W9 Forms, 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 APISet 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 ModelData 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": "name",
          "public_name": "Name",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "address",
          "public_name": "Address",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "city",
          "public_name": "City",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "state",
          "public_name": "State",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "zip_code",
          "public_name": "Zip Code",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "date",
          "public_name": "Date",
          "semantics": "date"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": true,
          "name": "employer_id",
          "public_name": "Employer ID",
          "semantics": "amount"
        }
      ],
      "features_name": [
        "name",
        "address",
        "city",
        "state",
        "zip_code",
        "date",
        "employer_id"
      ]
    }
  }
}
  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 filledDocument 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:

  • Name: type String that never contains numeric characters.
  • Address: type String without specifications.
  • City: type String that never contains numeric characters.
  • State: type String that never contains numeric characters.
  • Zip Code: type Number without specifications.
  • Date: type Date with US format.
  • Employer ID: type Number without specifications.

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

Document Data Model filledDocument Data Model filled

Train your W9 Form OCR

You’re all set! Now it's time to train your W9 Form deep learning model in the Training section of our API.
Train your modelTrain 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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