Bank Checks

This section describes how to build your custom OCR API to extract data from Bank checks using the API Builder. A Bank check is a written, dated, and signed piece of paper that directs a bank to pay a specific sum of money to the bearer.

Prerequisites

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

Define Your Bank Checks Use Case

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

• Issuer: The bank check's issuer's full name.
• Recipient: The bank check's recipient's full name.
• Amount: The bank check amount transferred from the issuer to the recipient.
• Date: The date the bank check was written.
• Check Number: The bank check Number (top hand right corner)

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 Bank checks, 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": "issuer",
          "public_name": "Issuer",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": 0 } },
          "handwritten": true,
          "name": "recipient",
          "public_name": "Recipient",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": true,
          "name": "amount",
          "public_name": "Amount",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "convention": "US" } },
          "handwritten": false,
          "name": "date",
          "public_name": "Date",
          "semantics": "date"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "check_number",
          "public_name": "Check Number",
          "semantics": "amount"
        }
      ],
      "features_name": ["issuer", "recipient", "amount", "date", "check_number"]
    }
  }
}

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. In our example, here are the different field configurations we used:

• Issuer: type String that never contains numeric characters.
• Recipient: type String that never contains numeric characters and that is mostly handwritten.
• Amount: type Number that is mostly handwritten.
• Date: type Date with US format that is mostly handwritten.
• Check Number: type Number without specifications.

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

Train Your Bank Checks OCR

You’re all set! Now it's the time to train your Bank checks 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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