Electricity Bill

This section describes how to build your custom OCR API to extract data from an Electric Bill using the API Builder. An Electric Bill document is a bill that a local utility issues to a consumer for the electricity their home consumes.


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

Define Your Electricity Bill Use Case

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

  • Electricity Provider: The electricity provider name
  • Customer Name: The full name of the customer
  • Account Number: The account number of the client
  • Bill Date: The bill issued date
  • Amount: The total due for this month
  • Phone number: The customer's phone number
  • Service Address: The customer's address related to the electricity bill

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 Electricity Bill, 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": "electricity_provider",
          "public_name": "Electricity provider",
          "semantics": "word"
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": false,
          "name": "customer_name",
          "public_name": "Customer name",
          "semantics": "word"
          "cfg": { "filter": { "alpha": 0, "numeric": -1 } },
          "handwritten": false,
          "name": "account_number",
          "public_name": "Account Number",
          "semantics": "word"
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "bill_date",
          "public_name": "Bill Date",
          "semantics": "date"
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "amount_due",
          "public_name": "Amount Due",
          "semantics": "amount"
          "handwritten": false,
          "name": "phone_number",
          "public_name": "Phone Number",
          "semantics": "phone"
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "service_address",
          "public_name": "Service address",
          "semantics": "word"
      "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:

  • Electricity provider: type String that never contains numeric characters.
  • Customer name: type String that never contains numeric characters.
  • Account Number: type String that never contains alpha characters.
  • Bill Date: type Date with US format.
  • Amount Due: type Number without specifications.
  • Phone Number: type Phone Number.
  • Service address: type String 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 filled

Train Your Electricity Bill OCR

You’re all set! Now it's time to train your Electricity Bill 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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