Rent Receipt

This section describes how to build your custom OCR API to extract data from Rent Receipts using the API Builder. A Rent Receipt is a document acknowledging that a person has received the rent for a rental property.

Prerequisites

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

Define your Rent Receipt Use Case

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

• Name: The landlord's full name
• Date: The date of the receipt
• Rent Amount: The collected rent amount
• Rent collection date: The rent collection date
• Rented good: The description of rented good

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 Rent Receipt, 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.

3. 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

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": { "convention": "US" } },
          "handwritten": false,
          "name": "date",
          "public_name": "Date",
          "semantics": "date"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "rent_amount",
          "public_name": "Rent Amount",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "rent_collection_date",
          "public_name": "Rent collection date",
          "semantics": "date"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "rented_good_description",
          "public_name": "Rented Good Description",
          "semantics": "word"
        }
      ],
      "features_name": [
        "name",
        "date",
        "rent_amount",
        "rent_collection_date",
        "rented_good_description"
      ]
    }
  }
}

2. Click on Upload a JSON config.
3. The data model will be automatically filled.
4. Click on Create API at the bottom of the screen.

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.
• Date: type Date with US format.
• Rent Amount: type Number without specifications.
• Rent collection date: type Date with US format.
• Rented Good Description: type String that never contains numeric characters.

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

Train your Rent Receipt OCR

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

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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